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Which article dynamically models the temporal sequence of single-frame distribution functions and differs from other articles?

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https://doi.org/10.1145/3477495.3532078
KEYWORDS
video moment retrieval; contrastive learning; cross-modal learning
ACM Reference Format:
Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei
Guo, Huyang Sun, and Yu-Gang Jiang. 2022. Video Moment Retrieval from
Text Queries via Single Frame Annotation. In Proceedings of the 45th Inter-
national ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR ’22), July 11–15, 2022, Madrid, Spain. ACM, New York, NY,
USA, 11 pages. https://doi.org/10.1145/3477495.3532078
1
INTRODUCTION
Video moment retrieval (VMR), initially proposed in [1, 12], is the
task of retrieving the segment described by a given natural lan-
guage query from an untrimmed video. This task, also known as
natural language video localization [25, 48, 52] and video temporal
grounding [5, 29], is a fundamental problem in computer vision
understanding and visual information retrieval. Differing to an ear-
lier task video action localization [11, 22], which aims at localizing
pre-defined categorized actions from the video, VMR is considered
as a more difficult task since the query is generalized to free natural
language thus involving with more complex cross-modal contents
understanding. VMR can be widely applied in many scenarios such
as video browsing websites and semantics based video search en-
gines.
To date, deep learning methods have approached VMR from two
directions differing in the way of data annotation. In building a
dataset of fully supervised VMR, given the target video, the annotator
is asked to choose a segment in the video and write a short text
query to describe the segment. In the meanwhile, the start and end
timestamps of this segment are noted down. Thus, one example in
the dataset is a quadruplet of video, query, start and end, denoted by
(𝑉,𝑄,𝑠𝑡,𝑒𝑑). Though many methods under fully supervised VMR [8,
12, 29, 42, 45, 48, 49] have achieved good performance, an obvious
disadvantage of this data annotating paradigm is its high time cost.
Besides, the annotation quality varies according to the annotator’s
subjective judgements, especially in determining the start and end:
the annotator is forced to give specific timestamps of the query, but
the video segment is often not separated to its context with clear
border. For example, to annotate the start and end of a query “the
man turns the light on”, one might consider the very second that
the switch is toggled should be the temporal interval, but another
arXiv:2204.09409v3  [cs.CV]  18 Jun 2022


Query: He flips the eggs, making an omelette.
Weakly Supervised
Annotation
Fully Supervised
Annotation
Glance Annotation
0 s
198.1 s
114.9 s
156.5 s
138.3 s
Figure 1: Illustration of different VMR training example an-
notation paradigms. The fully supervised setting marks the
start and end timestamps of the moment corresponding to
query. Weak supervision only annotates the video-text pair.
Our proposed glance annotation marks a single timestamp
in the moment.
might start the annotation from when the man walks towards
the switch. This makes fully supervised VMR prone to subjective
annotation noise. To avoid these disadvantages, weakly supervised
VMR is proposed [28], in which one example in the dataset is simply
(𝑉,𝑄), and no start nor end annotation is available. Though not
comparable to fully supervised VMR in performance, many studies
[17, 23, 27, 37, 39, 44] have shown that weak supervision is a feasible
compromise when the annotating resources are limited.
In our study, we argue that weak annotation can be augmented
with trivial cost and propose “glance annotation”, a new paradigm
of data annotation in VMR. As illustrated in Figure 1, a training
example under glance annotation is composed of (𝑉,𝑄,𝑔) in which
𝑔is any timestamp between 𝑠𝑡and 𝑒𝑑. This paradigm is motivated
by the fact that to annotate even a weak example, it is still inevitable
for one to watch the video in order to write the query, and very
often it is sufficient to know what the moment is about by watching
only a short snippet of it. Assuming that with properly designed
graphical user interface to support the annotation, one can note
down an instant timestamp 𝑔during “glancing” the video with no
more effort than a mouse click. Glance annotation not only largely
saves the time consumption in watching the video compared to
full supervision, but also provides more information than weak
supervision.
To validate the usability of glance annotation, we re-annotate
three publicly available fully supervised VMR datasets, namely
ActivityNet Captions [20], Charades-STA [12] and TACoS [33] by
substituting 𝑠𝑡and 𝑒𝑑with a uniformly sampled timestamp 𝑔in
range [𝑠𝑡,𝑒𝑑]. Under this setting, we propose a contrastive learning
based method named Video moment retrieval via Glance Annotation
(ViGA). Due to the lack of 𝑠𝑡and 𝑒𝑑, ViGA follows the multiple-
instance learning (MIL) strategy widely adopted in weakly super-
vised VMR, which uses the correspondence between 𝑉and 𝑄as the
supervision signal. In doing the training, the main objective is to
obtain a proper cross-modal encoder to project 𝑉and 𝑄to a joint
embedding space, which satisfies that the distance between the
embeddings of corresponding (𝑉,𝑄)pos is closer and the distance
between the embeddings of other combinations (𝑉,𝑄)neg is farther.
ViGA extends this idea by splitting 𝑉into multiple clips 𝐶and
learning in the granularity (𝐶,𝑄) instead, for making use of 𝑔by
enabling an assignment of different weights to all clips. Specifically,
we use heuristic Gaussian distribution peaking at the glance po-
sition to generate the weights. In doing the inference, we follow
the common proposal-based inference as in many weakly super-
vised methods, yet adjust the classical sliding window proposal
generation to an anchor driven proposal generation to better fit our
training strategy. To be specific, those sliding window proposals not
including a first selected anchor frame are pruned out. We enable
the network finding the anchor by adding an additional training
objective of focusing the attention of our multihead-attention [41]
based backbone to the glance position.
As will be shown in the rest of this paper, ViGA significantly
outperforms the state of the art of weakly supervised VMR, even
comparable to fully supervised VMR when a high precision of re-
trieved segment is not required. Our contributions are summarized
as follows:
• We propose glance annotation, a new VMR annotating para-
digm, which requires no more significant annotating effort
than weakly supervised VMR, while provides more potential
regarding the retrieval performance.
• We propose a contrastive learning based method ViGA for
glance annotated VMR, which achieves significantly better
performance than weakly supervised methods.
• We release a unified version of glance annotations on top of
three publicly available datasets ActivityNet Captions [20],
Charades-STA [12] and TACoS [33], to encourage future
research on this topic.
2
RELATED WORK
After initially proposed by [1, 12], early VMR studies mostly use
the annotated start and end timestamps for the video-text temporal
alignment learning, which we term as fully supervised VMR [1, 5, 6,
12, 18, 24, 29, 49]. Due to the expensive annotation cost, researchers
then began to exploit on learning under weak annotation with
video-text pairs only, which we term as weakly supervised VMR
[10, 13, 17, 23, 27, 28, 51].
2.1
Fully Supervised VMR
Existing fully supervised VMR methods can be categorized into two
groups. Two-stage methods [1, 12, 14, 18, 19, 24, 45] typically gen-
erate some pre-segmentation of proposal candidates using a sliding
window or other proposal networks, then input the generated pro-
posals and the text query separately into a cross-modal matching
network to predict matching confidence and select the best match-
ing segment as the output. Hendricks et al. [1] first proposed Mo-
ment Context Network (MCN), which generated proposals based on
sliding window, and then projected the video moment feature and
text query feature into a common representation space. Then they
used 𝐿2 distance as a measure to optimize triplet loss to narrow the
distance of positive samples and enlarge the distance of intra-video
and inter-video negative samples. Xu et al. [45] proposed Query-
guided Segment Proposal Network (QSPN) for alleviating the huge
computation burden caused by sliding window. Specifically, QSPN
integrated query features into video features to obtain attention
weights in time indexing, then combined it with 3D Region of In-
terest (ROI) pooling to obtain the sparse proposals. End-to-end
models [5, 6, 29, 43, 46, 47, 49] can be divided into anchor-based


Query: He flips 
the eggs, making 
an omelette.
3DCNN
GloVe
max pooling
QAG-KL   +   GLS-NCE
Cross-modal Representation Module
Gaussian Alignment Module
Bi-GRU 
Query Encoder
Query to Video
Cross-modal Encoder
Video to Query
Cross-modal Encoder
Self-attention
Video Encoder
glance
glance
Figure 2: Illustration of our training framework. In the Gaussian Alignment Module, a blue square denotes a word feature and
a yellow rectangle denotes the feature of one video frame. We use different heights of yellow squares to illustrate the different
weights of the frames. The solid and dashed green curves represent the heuristic Gaussian distribution generated with the
glance and the attention distribution generated by the model, respectively.
[5, 32, 43, 49] methods and anchor free [6, 7, 29, 46, 48] methods,
in which they differ in using / not using proposals in prediction,
respectively. As a typical work in the anchor-based category, Zhang
et al. [49] proposed 2D Temporal Adjacent Networks (2D-TAN) that
modeled the relations between segments of varying durations using
a two-dimensional feature map. The (𝑖, 𝑗)-th location of the feature
map indicated the start and end timestamps of the proposed seg-
ments. It then employed a Convolutional Neural Network (CNN) to
model the contextual interaction between various segments, using
ground truth labels to optimize the prediction score of each sug-
gestion in the feature map. For anchor-free methods, they usually
predict the probability of a frame being the start or end, or use a
neural network to directly regress the values of start and end. For
example, Lei et al. proposed XML [21] and used the 1D Convolu-
tional Start-End detector (ConvSE) to generate the start and end
scores on the late fused query-clip similarity matrix.
2.2
Weakly Supervised VMR
Although the fully supervised methods achieve good performance,
the expensive cost of annotating the temporal boundary limits
practical applications. Therefore, researchers recently began to pay
attention to the weakly supervised VMR [10, 13, 17, 23, 27, 28, 51].
Under the weakly supervised setting, we cannot obtain the detailed
start and end annotation of each query, only know whether the
query and video is a positive pair during training stage. Under this
constraint, most methods adopt the MIL framework. In MIL-based
VMR, the model learns the video-text alignment at video-level by
maximizing similarity scores of positive examples and suppress-
ing them on negative examples. Text-Guided Attention (TGA) [28]
was a typical pioneer work under the weak setting, which learned
text-aware video representation and leverages ranking loss to distin-
guish positive and negative samples. Ma et al. proposed VLANet [27]
which attempted to eliminate some irrelevant suggestions in the pro-
cess of MIL. Cross-sentence Relations Mining (CRM) [17] presented
by Huang et al. explored the temporal information modeling in
MIL using combinational associations among sentences. Semantic
Completion Network (SCN) [23] provided another reconstruction-
based idea of restoring the masked keywords in query according to
visual proposal and context information for the alignment learning
between modalities. Although weakly supervised VMR greatly re-
duces the burden of annotation, the performance of weak method
has a significant gap between the fully supervised method on the
test set.
3
METHODOLOGY
In this section, we first formally define the problem of glance an-
notated VMR and give an overview of our method ViGA. We then
introduce the two modules which form our training pipeline in
Section 3.3 and 3.4, respectively. The inference process is detailed
in Section 3.5.
3.1
Glance Annotation
Given an untrimmed video 𝑉and a text query 𝑄that semantically
describes a segment of the video, the VMR task aims at finding
the start and end timestamps 𝑠𝑡and 𝑒𝑑, such that moment 𝑉𝑠𝑡:𝑒𝑑
best corresponds to the query description. In fully supervised VMR,
complete human annotated 𝑠𝑡and 𝑒𝑑information is provided. In
contrast, under the weakly supervised VMR setting, only aligned
(𝑉,𝑄) pairs are available, with no fine-grained 𝑠𝑡or 𝑒𝑑information.
Our glance annotation scenario lies in between: a single timestamp
𝑔, satisfying 𝑠𝑡≤𝑔≤𝑒𝑑, is available at the training stage. We refer
to this timestamp 𝑔as a “glance”.
3.2
Algorithm Overview
Similar to the weakly supervised setting, it is not possible to let a
network learn to directly output 𝑠𝑡and 𝑒𝑑under glance annotation,
due to the lack of complete supervision signals. Instead, our method
selects a clip 𝐶from 𝑉that best matches 𝑄from a set of proposals
as the output. To learn this visual-textual alignment, many studies
in weakly supervised VMR adopt the MIL strategy and turn into
exploiting the correspondence of (𝑉,𝑄). Videos and queries that


we know are from the same example are marked as positive cor-
respondence (𝑉,𝑄)pos, while all other combinations in the batch
are treated as negative (𝑉,𝑄)neg. Our work extends this idea to
a finer-grained (𝐶,𝑄) level. Specifically, we build a network that
projects inputs from textual and visual modalities to a joint embed-
ding space, and train the network with a clip-to-query contrastive
objective, which pulls the distance between (𝐶,𝑄)pos closer and
pushes the distance between (𝐶,𝑄)neg farther.
Training. The overall structure of our training pipeline is il-
lustrated in Figure 2. After an initial feature extraction from pre-
trained models, our Cross-modal Representation Module encodes the
two input modalities by first applying two independent uni-modal
encoders, and then cross-interacting the two uni-modal features
to each other. As a result, token-level (words for text and frames
for video) cross-modal features are obtained. The Query to Video
Cross-modal Encoder additionally outputs an attention distribution
across all video frames. To train the network, we propose a Gauss-
ian Alignment Module, in which we generate a heuristic Gaussian
distribution peaking on the glance timestamp. All video frame fea-
tures are weighted by this heuristic distribution in calculating our
Gaussian Label-Smoothed Noise Contrastive Estimation loss (GLS-
NCE). The same Gaussian heuristic distribution is further used
in our Query Attention Guide Kullback–Leibler Divergence loss
(QAG-KL) to guide the learning of our network. The total loss of
our network is a fusion of the two loss functions.
Inference. To align with the training design, we propose a cor-
responding Query Attention Guided Inference strategy. After the
network forward pass up to the Cross-modal Representation Module,
the frame that gathers the most attention in Query to Video Cross-
modal Encoder is marked as the anchor frame. We sample proposals
of different sizes around this anchor frame (i.e., a proposal must
contain the anchor frame) and form a pool of proposals. The pro-
posal that gets the highest dot-product similarity to the sentence
feature is selected as the final output.
3.3
Cross-modal Representation Module
Given a video𝑉= [𝑣1, ..., 𝑣𝐿𝑣], and a query 𝑄= [𝑞1, ...,𝑞𝐿𝑞], we en-
code deep features of the two inputs using the proposed Cross-modal
Representation Module. Specifically, we first use two independent
encoders to ensure sufficient understanding of uni-modal semantics
for video and query. Next, to enable the cross-modal learning, we
fuse the semantics of the two modalities in the subsequent bidirec-
tional cross-modal encoder. As a result, per-token representations
v ∈R𝐿𝑣×𝑑model and q ∈R𝐿𝑞×𝑑model are obtained, where 𝑑model is
the dimension of the joint embedding feature (and also the overall
hidden dimension of our network).
Query Encoding. A bidirectional Gated Recurrent Unit (GRU)
is applied to encode the sequential semantics of all 𝐿𝑞words in 𝑄,
taking word embeddings from the pre-trained GloVe [31] model as
input. A word-level feature q𝑖is the concatenation of the forward
and backward hidden states of the final layer of the GRU, given by
q𝑖= [
→
h𝑖;
←
h𝑖] ∈R𝑑model.
(1)
·
·
·
Someone puts onions 
in the skillet and beats 
eggs into a bowl 
before adding them to 
the skillet.
The coach helps the 
guy in red with the 
proper body placement 
and lifting technique.
A band plays music on 
stage.
·
·
·
·
·
·
Figure 3: Illustration of clip-level MIL training strategy in
one batch in the Gaussian Alignment Module. Green solid
lines indicate positive correspondences, and gray dashed
lines indicate negative matching.
Video Encoding. For an untrimmed video, we first extract fea-
tures using a pre-trained CNN, such as C3D [40], I3D [4] and VGG
[36], followed by a fully connected layer to map the feature dimen-
sion to 𝑑model. To encode the sequential semantics of the extracted
video feature v, we apply a multihead self-attention module [41]
across all the frames. The encoding at the 𝑖-th frame is given by
Attn(𝑄(v𝑖), 𝐾(v),𝑉(v)) = softmax(𝑄(v𝑖)𝐾(v)𝑇
√︁
𝑑model/ℎ
)𝑉(v),
(2)
where 𝑄(·), 𝐾(·) and 𝑉(·) are three independent linear transforma-
tions from 𝑑model to 𝑑model, and ℎdenotes the number of heads.
Cross-modal Encoding. To fuse the information from the two
modalities, we apply cross-modal multihead attention after the
individual uni-modal self encoding, i.e., using one modality as query
and the other as key and value. In this way, the cross-encoding of
the 𝑖-th word is given by
Attn(𝑄(q𝑖), 𝐾(v),𝑉(v)) = softmax(𝑄(q𝑖)𝐾(v)𝑇
√︁
𝑑model/ℎ
)𝑉(v),
(3)
and the cross-encoding of the 𝑖-th frame is given by
Attn(𝑄(v𝑖), 𝐾(q),𝑉(q)) = softmax(𝑄(v𝑖)𝐾(q)𝑇
√︁
𝑑model/ℎ
)𝑉(q).
(4)
For each encoding module in the uni-modal encoding and the
cross-modal encoding, the module is subsequently followed by a
two-layer feed-forward module activated by ReLU [30] to further
enhance the encoding capacity. Moreover, we follow the standard
configuration of multihead attention modules, where layernorm
[2], dropout [38], position embedding [9] and residual connection
[16] are applied.
3.4
Gaussian Alignment Module
In MIL-based methods under weakly supervised VMR, the general
paradigm is to learn proper deep representation 𝑓𝑣(𝑉) ∈R𝑑model
and 𝑓𝑞(𝑄) ∈R𝑑model that corresponding pairs align closer to each
other via contrastive learning. We extend this idea of video-level


MIL and propose a Gaussian Alignment Module that transforms the
problem to a finer-grained clip-level MIL to train the preceding
Cross-modal Representation Module. Our motivation is that the exis-
tence of glance 𝑔makes frames in the video in-equally important
in terms of the relevance to the query. For a frame 𝑣𝑖, the relevance
is higher when its temporal distance to 𝑔is closer: consider a long
video including scene change, the frames that are too far away
from 𝑔might contain totally irrelevant semantics. Mathematically,
Gaussian distribution has the characteristic that the highest proba-
bility value at the mean point and gradually reduces the probability
to both sides, which aligns consistently to our motivation. Thus,
we use Gaussian distribution to model this relevance. As illustrated
in Figure 3, all video frames are assigned with Gaussian-distributed
weights where the peak position of Gaussian is the glance 𝑔. To
get the weight of the 𝑖-th frame, we scale the index 𝑖∈{1, 2, ..., 𝐿𝑣}
into the domain [−1, 1] by linear transformation
𝑓(𝑖) = (𝑖−1) ·
2
𝐿𝑣−1 −1,
(5)
and sample the Gaussian values via the probability density function
𝐺(𝑖) = norm(
1
√
2𝜋𝜎
exp(−(𝑓(𝑖) −𝑓(𝑔))2
2𝜎2
)),
(6)
where 𝜎is a hyperparameter, and the normalization scales 𝐺(𝑖)
where 𝑖∈[−1, 1] into range [0, 1].
After different weights are assigned across the video frames,
we are able to get video clips with different weights as training
examples. A sliding window of size 𝐿𝑐with stride 𝑠is applied on
the video to get clips. Each clip is then max pooled along the frame
dimension to generate the clip-level feature in the joint embedding
space R𝑑model. To this end, the 𝑖-th clip feature c𝑖is given by
c𝑖= max_pool([v(𝑖−1) ·𝑠+1, ..., v(𝑖−1) ·𝑠+𝐿𝑐]) ∈R𝑑model.
(7)
And each clip is assigned with a clip-level weight 𝑤𝑖sampled at
the middle point of the clip, given by
𝑤𝑖= 𝐺((𝑖−1) · 𝑠+ 𝐿𝑐
2 ).
(8)
Similarly, for the text modality, sentence-level feature s is ob-
tained by max pooling its word-level features, given by
s = max_pool([q1, ..., q𝐿𝑞]) ∈R𝑑model.
(9)
GLS-NCE Loss. In weakly supervised VMR, standard NCE loss
on video level can be directly applied to train the video and query
encoders 𝑓𝑣(·) and 𝑓𝑞(·) by contrasting (𝑉,𝑄)pos against (𝑉,𝑄)neg
in one batch. For one video 𝑉in a batch of 𝐵video query pairs,
there is only one positive matching query 𝑄𝑝, and the rest 𝐵−1
queries are negative queries 𝑄𝑛. Therefore, the standard video-level
NCE loss is given by
LVideo-NCE = −log(
𝑒𝑓𝑣(𝑉)⊤𝑓𝑞(𝑄𝑝)
𝑒𝑓𝑣(𝑉)⊤𝑓𝑞(𝑄𝑝) +
𝐵−1
Í
𝑖=1
𝑒𝑓𝑣(𝑉)⊤𝑓𝑞(𝑄𝑛
𝑖)
).
(10)
·
·
·
·
·
·
Video Frame
Anchor Frame
Sliding Window
Sliding window based inference
Our proposed QAGI
Figure 4: Comparison between sliding window based infer-
ence and our proposed Query Attention Guided Inference.
However, in our method, the proposed GLS-NCE loss is built on
clip level. Each video is substituted by 𝑁clips as in Equation 7. On
this basis, for a video in the same batch of size 𝐵, clip-level NCE
loss is extended to
LClip-NCE = −log(
𝑁
Í
𝑖=1
𝑒c⊤
𝑖𝑓𝑞(𝑄𝑝)
𝑁
Í
𝑖=1
𝑒c⊤
𝑖𝑓𝑞(𝑄𝑝) +
𝑁
Í
𝑖=1
𝐵−1
Í
𝑗=1
𝑒c⊤
𝑖𝑓𝑞(𝑄𝑛
𝑗)
).
(11)
Additionally, the clips also differ in weights given by Equation 8.
To accommodate this, we implement clip-level NCE in the form of
cross-entropy following MoCo [15], and enable the weighting via
label smoothing. In this GLS-NCE loss, the Gaussian weight 𝑤𝑖of
a clip c𝑖is assigned as the label smoothing amount, i.e., instead of
using a one-hot label across the 𝐵queries in the batch, we assign
𝑤𝑖to the label of the positive query, and smooth the rest 𝐵−1
negative labels to 1−𝑤𝑖
𝐵−1 . In summary, for a clip c𝑖with weight 𝑤𝑖,
its GLS-NCE loss is given by
LGLS-NCE = 𝑤𝑖· log(c⊤
𝑖𝑄𝑝) +
𝐵−1
∑︁
𝑗=1
1 −𝑤𝑖
𝐵−1 log(c⊤
𝑖𝑄𝑛
𝑗).
(12)
QAG-KL Loss. To further smooth the learning and to align with
the inference strategy to be explained in Section 3.5, we leverage
the nature of attention mechanism [41] in our encoding module
and propose the QAG-KL loss. Specifically, we use a KL divergence
between the attention distribution of the Query to Video Cross-
modal Encoder (Equation 3) and the Gaussian guidance (Equation
6), to pull the attention distribution across all video frames closer
to the Gaussian guidance. Since the query 𝑄contains 𝐿𝑞words, we
treat them equally and use the mean of their attention distributions
as the sentence-level attention distribution. For the 𝑖-th frame in
the video, the QAG-KL loss is given by
LQAG-KL = 𝐺(𝑖)(log𝐺(𝑖) −a𝑖)
(13)
where a =
𝐿𝑞
Í
𝑗=1
softmax( q𝑗𝐾(v)𝑇
𝑑model/ℎ)
𝐿𝑞
∈R𝐿𝑣.
(14)


The complete loss function of a batch is the combination of the
GLS-NCE loss across all clips in the batch and the QAG-KL loss
across all frames of all videos in the batch, given by
L =
∑︁
c
LGLS-NCE +
∑︁
𝑣
∑︁
𝐿𝑣
LQAG-KL.
(15)
3.5
Query Attention Guided Inference
Due to the lack of 𝑠𝑡and 𝑒𝑑annotations, weakly supervised VMR
methods often compromise to designing two independent pipelines
for training and inference. Under weakly supervised VMR, the most
common inference strategy is to select the best proposal from a se-
ries of proposals generated by methods like sliding window. Despite
that it is still not possible to design a unified pipeline that handles
training and inference consistently under glance annotation, we
propose to use a Query Attention Guided Inference (QAGI) to best
align the inference process to our aforementioned training strategy.
As illustrated in Figure 4, given a video 𝑉and query 𝑄, we first
extract the features v ∈R𝐿𝑣×𝑑model and s ∈R𝑑model via the trained
cross-modal representation module as described in previous sec-
tions. After that, we select an anchor point index 𝑎𝑝∈{1, 2..., 𝐿𝑣}
with the guidance of the query to video attention distribution.
Specifically, the frame where the attention value reaches its maxi-
mum is chosen as the anchor frame, given by
𝑎𝑝= arg max
𝑖
a𝑖.
(16)
A series of proposals are then generated around 𝑎𝑝, i.e., we first
apply a naive sliding window on the 𝐿𝑣frames to generate a pro-
posals pool {𝑝𝑖:𝑗}, then prune out all proposals that does not satisfy
𝑖≤𝑎𝑝≤𝑗. On this basis, the proposal that maximizes the similarity
score to the query is select as our final output, given by
arg max
𝑖,𝑗
max_pool([v𝑖:𝑗])⊤s.
(17)
4
EXPERIMENTS
To validate our proposed glance annotation and the method ViGA,
extensive experiments are conducted on three publicly available
datasets. We also perform ablation studies on different components
in ViGA to investigate their influence in details.
4.1
Datasets
We re-annotate the following datasets to fit in our proposed glance
annotation. Specifically, we augment each example with a uniformly
sampled timestamp 𝑔in range [𝑠𝑡,𝑒𝑑].
ActivityNet Captions. Krishna et al. [20] annotated the Activ-
ityNet v1.3 dataset [3] which was originally designed for video
captioning, and released the ActivityNet Captions dataset for VMR.
It contains 19,994 YouTube videos from diverse domains. Follow-
ing previous studies [49, 50], we use the pre-defined split val_1
as validation set and test on val_2. As a result, 37,421, 17,505, and
17,031 annotations are used for training, validating, and testing,
respectively.
Variants
R@IoU=
mIoU
0.3
0.5
0.7
Video-NCE
35.58
18.30
8.54
25.34
Clip-NCE
16.72
6.25
2.02
14.93
GLS-NCE
59.61
35.79
16.96
40.12
Table 1: Ablation comparison among training with different
NCE loss functions on ActivityNet Captions dataset.
Variants
R@IoU=
mIoU
0.3
0.5
0.7
w/o QAG-KL
54.74
34.26
16.68
37.96
w/ QAG-KL
59.61
35.79
16.96
40.12
Table 2: Ablation comparison between model trained with
and without QAG-KL loss on ActivityNet Captions dataset.
Variants
R@IoU=
mIoU
0.3
0.5
0.7
Sliding Window
58.13
31.23
13.62
38.24
Query Attention Guided Inference
59.61
35.79
16.96
40.12
Table 3: Ablation comparison of inference with query atten-
tion guided inference / naive sliding window on ActivityNet
Captions dataset.
Charades-STA. Gao et al. [12] annotated the Charades dataset
[35] using a semi-automatic approach and formed the Charades-
STA dataset. It contains 9,848 videos of daily indoors activities.
We follow the standard split of 12,408 and 3,720 annotations for
training and testing defined by the annotator.
TACoS. Regneri et al. [33] annotated the MPII Cooking Compos-
ite Activities dataset [34] which was originally designed for activity
recognition, and formed the TACoS dataset. It contains 127 videos
of cooking. We follow the standard split provided by [12], and 9790,
4436, and 4001 annotations are included in training, validation and
test set, respectively.
4.2
Evaluation Metric
We evaluate our method using 1) recall of threshold bounded tem-
poral intersection over union (R@IoU), which measures the per-
centage of correctly retrieved predictions where only the temporal
IoU between the prediction and the ground truth greater than a
certain threshold is accepted, and 2) mean averaged IoU (mIoU)
over all predictions.
4.3
Implementation Details
We fix the 3D CNN modules for extracting visual features for a
fair comparison. For all the three datasets, we use C3D as feature
extractor. Since Charades-STA lacks a unified standard of feature
extractor in previous studies, additional experiments using I3D and
VGG features are also conducted for the completeness of compari-
son. For the word embedding, we adopt 840B GloVe for building a
most complete vocabulary. To increase the capacity of our encoders,
we stack two layers of our query, video and cross-modal encoders.


0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
sigma
10
20
30
40
50
60
Recall@1
IoU=0.3
IoU=0.5
IoU=0.7
mIoU
36
37
38
39
40
mIoU
Figure 5: Influence of the hyperparameter 𝜎of the Gaussian
distribution in Equation 6 on ActivityNet Captions dataset.
The model dimension 𝑑model is set to 512, and the number of atten-
tion heads ℎis set to 8 globally. Our model is trained with AdamW
[26] with a learning rate of 0.0001 half decaying on plateau. We
clip the gradient norm to 1.0 during training. The batch size and
𝜎factor of the three datasets are empirically set to (256, 0.4), (256,
0.3) and (128, 1.0), respectively. All experiments are conducted on a
Nvidia Tesla V100 GPU with 32GB memory.
4.4
Ablation Studies
To evaluate the effectiveness of different components in our pro-
posed ViGA, we conduct extensive ablation experiments on the
ActivityNet Captions dataset.
Effects of GLS-NCE. In order to verify the effectiveness of our
proposed GLS-NCE loss (Equation 12), we compare it with the
aforementioned variants Video-NCE loss (Equation 10) and Clip-
NCE loss (Equation 11). The Video-NCE treats the video as a whole
and maximizes the similarity between it and the text query. The
Clip-NCE cuts a video into many clips, which increases the number
of examples in the batch. However, as the distance between the
clip and glance increases, its relevance to the query becomes lower.
Therefore, our GLS-NCE assigns different weights to different clips
according to their temporal distances to the glance. The results
are listed in Table 1. The performance of GLS-NCE is significantly
ahead of others, thus showing its effectiveness. Besides, it is worth-
while to note that scores of Clip-NCE are almost half of Video-NCE,
indicating that simply increasing the number of samples through
clip segmentation is not beneficial, but sharply harms the perfor-
mance instead. Comparing the three groups of experiments, we
conclude that the performance improvement of GLS-NCE is not
brought by increasing the number of examples by slicing a video
into clips, while the enhancement from Gaussian label smoothing
makes the main contribution.
Effects of QAG-KL. The QAG-KL loss (Equation 14) encourages
the model to pay more attention to the glance frame and its near
neighbors in the training stage. To validate the its effectiveness, we
conduct the ablation study of simply removing the QAG-KL loss.
From the results in Table 2, we have the following observations.
First, QAG-KL improves the moment retrieval performance on all
evaluation metrics. This shows that in the training stage, QAG-KL
can indeed make use of the prior information of glance annotation
and help the model with better cross-modal alignment learning.
Second, the performance with QAG-KL increases more significantly
when the IoU threshold is 0.3 than other thresholds, reaching around
5%. We consider this gap is due to the fact that glance is a relatively
weak prior information, so it performs better when the requirement
of retrieval precision (reflected by the IoU) is not strict.
Sliding Window vs. Query Attention Guided Inference. To
verify the effectiveness of our proposed QAGI, we evaluate the
same trained model under different testing strategy, i.e., naive slid-
ing window vs. QAGI. The results in Table 3 show that QAGI has
advantages over the traditional sliding window based evaluation on
all metrics. QAGI uses the attention matrix learned in the training
stage to obtain the anchor frame for generating proposals in the
test stage, which can filter out irrelevant proposals to a great extent,
especially those with short durations. It is worthwhile to note that
the improvement is more obvious under the metric with larger IoU
threshold, as the performance raises by 4.5% and 3.3% respectively
at IoU threshold of 0.5 and 0.7. This suggests that using the anchor
is beneficial especially when the retrieval precision requirement is
relatively high.
Effects of the Gaussian distribution parameter 𝝈. In this
ablation study, we focus on the hyperparameter 𝜎in Equation 6.
Theoretically, 𝜎describes the dispersion degree of a Gaussian distri-
bution: the larger the 𝜎, the flatter the curve. In the context of our
Gaussian Alignment Module, the value of 𝜎controls to what extent
that the weight at the glance frame which is always 1.0 disperses to
other frames, hence affecting the overall positiveness of all the clips
in the video. Consider an extreme example, when 𝜎takes a very
large value, all frames in the video are assigned with weights close
to 1.0. This means that we take all clips almost equally positive,
which reduces the learning to be approximately equivalent to the
video-level MIL under weak supervision. Therefore, choosing an ap-
propriate 𝜎is important. As reported in Figure 5, as 𝜎increases, the
performance of the four metrics first increases and then decreases.
Specifically, when 𝜎is set to 1.2, i.e., we over-assign positiveness
to the clips, the performance of the four metrics decreases sharply
(e.g., mIoU decreases from 40 to 36). On the other hand, when 𝜎is
very small, i.e., we only take a very narrow range of video clips as
important positive examples, the performance decreases because of
losing some clips that are in fact informative positive examples (e.g.,
when 𝜎is set to 0.05, mIoU decreases by 2%). On the ActivityNet
Captions dataset, the performance achieves its best when 𝜎is set to
a medium value 0.4. This observation coincides with our theoretical
analysis.
4.5
Comparison with State of the Art
We compare the proposed ViGA with both fully and weakly super-
vised methods, which are introduced as follows.
Compared Methods. As shown in Table 4, we divide the com-
pared methods into three sections according to the supervision


Supervision
Method
Charades-STA
ActivityNet Captions
TACoS
R@0.3
R@0.5
R@0.7
mIoU
R@0.3
R@0.5
R@0.7
mIoU
R@0.3
R@0.5
R@0.7
mIoU
Full
Supervision
CTRL [12]
-
23.63
8.89
-
-
-
-
-
18.32
13.3
-
-
QSPN [45]
54.7
35.6
15.8
-
45.3
27.7
13.6
-
-
-
-
-
2D-TAN [49]
-
39.7
23.31
-
59.45
44.51
26.54
-
37.29
25.32
-
-
LGI [29]
72.96
59.46
35.48
51.38
58.52
41.51
23.07
41.13
-
-
-
-
VSLNet [48]
70.46
54.19
35.22
50.02
63.16
43.22
26.16
43.19
29.61
24.27
20.03
24.11
Weak
Supervision
TGA [28]
32.14
19.94
8.84
-
-
-
-
-
-
-
-
-
SCN [23]
42.96
23.58
9.97
-
47.23
29.22
-
-
-
-
-
-
BAR [44]
44.97
27.04
12.23
-
49.03
30.73
-
-
-
-
-
-
VLANet [27]
45.24
31.83
14.17
-
-
-
-
-
-
-
-
-
MARN [37]
48.55
31.94
14.81
-
47.01
29.95
-
-
-
-
-
-
LoGAN [39]
51.67
34.68
14.54
-
-
-
-
-
-
-
-
-
CRM [17]
53.66
34.76
16.37
-
55.26
32.19
-
-
-
-
-
-
Glance
Supervision
2D-TAN [49] †
-
-
-
-
11.26
5.28
2.34
-
13.97
5.50
1.60
-
LGI [29] †
51.94
25.67
7.98
30.83
9.34
4.11
1.31
7.82
-
-
-
-
ViGA (C3D)
56.85
35.11
15.11
36.35
59.61
35.79
16.96
40.12
19.62
8.85
3.22
15.47
ViGA (VGG)
60.22
36.72
17.20
38.62
-
-
-
-
-
-
-
-
ViGA (I3D)
71.21
45.05
20.27
44.57
-
-
-
-
-
-
-
-
Table 4: Performance comparison with the state-of-the-art methods under different supervision settings. “†” denotes our re-
implemented results of fully supervised methods under glance annotations. In order to align with their original design, we
give a relaxed glance condition by shrinking the original annotations to a random 3-seconds duration instead of one instant
timestamp as in our results.
types, including full supervision, weak supervision and glance su-
pervision. When selecting methods from the literature to compare,
we follow the rule of diversely selecting representative methods in
different categories as introduced in Section 2 for the completeness
of the comparison. For two-stage fully supervised methods, CTRL
[12] is sliding window based and QSPN [45] is proposal based. In
the end-to-end fully supervised methods, 2D-TAN [49] belongs
to anchor based, while LGI [29] and VSLNet [48] are anchor free.
For weak supervision, a dominant number of methods adopt MIL
strategy than query reconstruction. Therefore, we select MIL-based
methods like TGA [28], VLANet [27], LoGAN [39], CRM [17] and
one representative reconstruction-based method SCN [23]. Cur-
rently, CRM is the state of the art in weakly supervised VMR.
In addition to these existing studies, we apply glance annotation
to two well-recognized fully supervised methods (i.e., 2D-TAN and
LGI) for a more direct comparison to our proposed ViGA. In order
to align with their original design, we give a relaxed glance condi-
tion by shrinking the original annotations to a random 3-seconds
duration instead of one instant timestamp. Practically, we achieve
this by directly changing the annotations in the data and run their
publicly available source codes.
Observations and Discussions. According to the results in Ta-
ble 4, we can make a number of observations worthy discussing.
1. In terms of all metrics, our proposed approach significantly
exceeds the methods under weak supervision on the Charades-STA
and ActivityNet Captions dataset. We improve the recall by 7%,
11% and 4% on Charades-STA when IoU is 0.3, 0.5 and 0.7, respec-
tively. On ActivityNet Captions, the improvement is 5% and 4%
when IoU is 0.3 and 0.5, respectively. We believe that on one hand,
it shows that the setting of glance annotation is reasonable and
has good potential in performance, and on the other hand, it also
shows that ViGA succeeds in exploiting the information provided
by glance annotation. In addition, in order to make ViGA standard
and concise, we did not use some effective tricks in weak super-
vision methods, such as surrogate proposal selection in VLANet
and temporal semantic consistency in CRM. This may take the
performance of ViGA further, and we leave this as future work.
2. When comparing to some fully supervised methods, we are
surprised to find that when IoU is small (e.g., 0.3), our method almost
reaches a same performance level. For example, on Charades-STA,
our R@1 IoU=0.3 is 71.21%, 1.6% lower than LGI and 0.8% higher
than VSLNet. On ActivityNet Captions, the recall is 59.61%, 1.9%
higher than LGI and 3.6% lower than VSLNet. This suggests that
under the scenario of coarse-grained retrieval requirements, glance
annotation might be more advantageous than full annotation with
acceptable performance yet significantly lower cost. However, there
is still a lot of space for improvement when a high retrieval precision
is required. For example, when the R@1 IoU=0.7, the performance
gap between ViGA and LGI on Charades-STA reaches 15.21%.
3. For the previously mentioned fully supervised method re-
implemented under relaxed glance annotation, we have the follow-
ing findings. First, although we relax the setting of glance to 3 sec-
onds, our approach shows superior performance in all three datasets.
Second, we observe that the performance of re-implemented meth-
ods on ActivityNet Captions is not sufficiently satisfying. Therefore,
it might be inadvisable to transplant fully supervised methods to
glance annotation setting by directly changing the annotation to a
instant moment or a short time duration, especially for the dataset
like ActivityNet Captions, which generally has a long video dura-
tion and a wide range of moment lengths.
4. As can be seen from Table 4, weakly supervised methods are
often not tested on TACoS dataset because the videos in TACoS are


Query: A small child is seen standing on a base with an older man pointing.
Ground Truth
Prediction
Q2V 
Attention
Video (1)
0.0s - 25.5s
0.0s - 24.7s
Attn_max=11.2s
Video duration=124.2s
Query: They put soap on their hands and scrub them together.
Ground Truth
Prediction
Q2V 
Attention
Video (2)
7.6s - 75.0s
18.9s - 81.8s
Glance=1.2s,  Attn_max=11.2s
Video duration=89.8s
Attn_max=59.3s
Query: He gets down on the ground and flips around.
Ground Truth
Prediction
Q2V 
Attention
Video (3)
43.1s - 55.5s
9.3s - 66.6s
Glance=1.2s,  Attn_max=11.2s
Video duration=73.1s
Attn_max=24.0s
Figure 6: Some visualized examples from the test split of ActivityNet Captions. The first two examples are successful and the
third is a failing case.
very long and the moments to be retrieved are too short, i.e., the
requirement of retrieval precision is very high. It might be hard for
existing weakly supervised methods to deal with this situation. Our
proposed ViGA shows positive in such case with a similar result to
early fully supervised methods, such as CTRL.
4.6
Qualitative Analysis
Figure 6 shows some qualitative examples from the test split of
ActivityNet Captions dataset, in which the green bar is the ground
truth temporal boundary of the language query and the blue bar
represents the predicted boundary of ViGA. We also visualize the
query-to-video attention (pink curve under the video flow) to il-
lustrate our proposed QAG-KL loss and query attention guided
inference. Video (1) and Video (2) are successfully retrieved sam-
ples with high IoU. They show the effectiveness of our method
from two aspects. For video (1), the video duration is very long
(up to 124.2 seconds) and the moment to be retrieved is relatively
short (25.5 seconds), which reveals that our proposed approach
based on glance annotation can locate precisely when the video
semantics is complex. As can be seen from the plot, this is benefited
from a reasonable query-to-video attention distribution which is
precisely positioned in the correct moment interval. On one hand, it
enhances the cross-modal representation learning, and on the other
hand, it provides a good anchor frame for inference. For video (2),
we observe that ViGA successfully retrieves this long moment of
nearly one minute. Given that we might be able to have good results
of retrieving long segments under single frame glance annotation,
it is reasonable to conjecture that the length of full annotation could
have been reduced, even not to the extreme of just one single frame.
Therefore, our qualitative results are in favor of the great potential
of glance annotation. Inevitably, there are also failing cases. For
example, in Video (3), the language query corresponds to a short
clip of the man gets down on the ground and flips around, but our
model recalls a long range segment containing the man, includ-
ing a large part of the man standing, showing a lack of sufficient
understanding of the fine-grained textual semantics. We consider


that this is the hard part in the task of retrieving video moments
with free-form text query. There is not sufficiently large amount
of training data for learning fine-grained semantics because the
free-form text query has great diversity. The model can be easily
confused about some fine-grained actions, such as “get down on
the ground and flip around” here.
5
LIMITATIONS
Due to our limited resource, we are only able to re-annotate the
datasets in an automatic way by doing random sample in the time in-
terval of original annotation instead of manually annotating them.
Although we achieve good results in the previous experiments,
there are some inevitable problems in this simple re-annotation
strategy. For example, some queries might contain multiple seman-
tics, which are not possible to be captured by only one glance. Also,
in some rare cases, meaningless frames that would pollute the train-
ing data such as camera scene changes might be sampled as the
glance, which could have been filtered out manually. We hope a
manually annotated dataset in glance annotation could be collected
in the future to support follow-up research in this direction.
6
CONCLUSION
In this paper, we study the problem of VMR. After analysing the
advantages and limitations of the two existing VMR paradigms fully
supervised VMR and weakly supervised VMR, we find that weakly
supervised VMR can be augmented with trivial cost, and propose a
new data annotation paradigm named as glance annotation. Under
glance annotation, we propose ViGA, a novel clip-level contrastive
learning framework, as a pioneer method to solve this problem.
Extensive experiments are conducted on three publicly available
datasets ActivityNet Captions, Charades-STA and TACoS, and ViGA
outperforms existing weakly supervised methods by a large margin.
Therefore, we conclude that glance annotation is a promising new
data annotation paradigm for VMR, and ViGA is a feasible method
for glance annotated VMR. Our results support further research
and applications of glance annotation in real-life problems.
ACKNOWLEDGEMENT
This research is conducted within the first two authors’ intern-
ship in bilibili. We are grateful to bilibili AI for the support and
inspiration.
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D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with
Glance Annotation
Hanjun Li1, Xiujun Shu1, Sunan He2, Ruizhi Qiao1, Wei Wen1, Taian Guo1, Bei Gan1, Xing Sun1*
1Youtu Lab, Tencent
2Hong Kong University of Science and Technology
{hanjunli, xiujunshu, ruizhiqiao, jawnrwen, taianguo, stylegan, winfredsun}@tencent.com
sunan.he@connect.ust.hk
Abstract
Temporal sentence grounding (TSG) aims to locate a
specific moment from an untrimmed video with a given nat-
ural language query. Recently, weakly supervised methods
still have a large performance gap compared to fully super-
vised ones, while the latter requires laborious timestamp
annotations. In this study, we aim to reduce the annotation
cost yet keep competitive performance for TSG task com-
pared to fully supervised ones. To achieve this goal, we
investigate a recently proposed glance-supervised temporal
sentence grounding task, which requires only single frame
annotation (referred to as glance annotation) for each
query. Under this setup, we propose a Dynamic Gaussian
prior based Grounding framework with Glance annotation
(D3G), which consists of a Semantic Alignment Group Con-
trastive Learning module (SA-GCL) and a Dynamic Gaus-
sian prior Adjustment module (DGA). Specifically, SA-GCL
samples reliable positive moments from a 2D temporal map
via jointly leveraging Gaussian prior and semantic consis-
tency, which contributes to aligning the positive sentence-
moment pairs in the joint embedding space. Moreover, to al-
leviate the annotation bias resulting from glance annotation
and model complex queries consisting of multiple events,
we propose the DGA module, which adjusts the distribu-
tion dynamically to approximate the ground truth of tar-
get moments. Extensive experiments on three challenging
benchmarks verify the effectiveness of the proposed D3G.
It outperforms the state-of-the-art weakly supervised meth-
ods by a large margin and narrows the performance gap
compared to fully supervised methods. Code is available at
https://github.com/solicucu/D3G.
1. Introduction
Temporal sentence grounding is a fundamental prob-
lem in computer vision and receives an increasing atten-
*Corresponding author
Query: person pours some water into a glass.
5.9 s    (8.0 s)   15.4 s
𝑔
positive pair
negative pair
slide window
ViGA
Duration: 21.96 s
DGA
positive pair
negative pair
D3G
2d map
5.9 s    (8.0 s)   15.4 s
𝑔
Figure 1. Illustration of glance annotation g (red dashed line) and
simple comparison between ViGA and D3G. The red rectangle
indicates the boundary of target moment.
tion in recent years.
Given the query sentence and an
untrimmed video, the goal of TSG is to localize the start and
end timestamps of specific moment that semantically corre-
sponds to the query. In recent years, full supervised tem-
poral sentence grounding (FS-TSG) has achieved tremen-
dous achievements [9, 1, 41, 43, 34, 29, 33, 42]. However,
obtaining accurate timestamps for each sentence is labor-
intensive and subjective, which prevents it from scaling to
large-scale video-sentence pairs and practical applications.
Weakly supervised temporal sentence grounding (WS-
TSG), which requires only the video and query pairs, re-
ceives an increasing attention recently. Although great ad-
vances [19, 32, 12, 45, 43, 44] have been achieved in recent
years, there still remains a huge performance gap between
WS-TSG and FS-TSG. WS-TSG suffers from severe local-
ization issues due to the large discrepancy between video-
level annotations and clip-level task.
Recently, Cui et al. [6] propose a new annotating
paradigm called glance annotation for TSG, requiring the
timestamp of only random single frame within the tempo-
ral boundary of the target moment. It is noted that such
annotation only increases trivial annotating cost compared
to WS-TSG. Figure 1 illustrates the details of glance an-
notation. With glance annotation, Cui et al. propose the
arXiv:2308.04197v1  [cs.CV]  8 Aug 2023


ViGA based on contrastive learning. ViGA first cuts the in-
put video into clips of fixed length, which are assigned with
Gaussian weights generated according to the glance anno-
tation, and contrasts clips with queries. There are two ob-
vious disadvantages in this way. First, moments of interest
usually have various durations. Therefore, these clips can-
not cover a wide range of target moments, which inevitably
aligns the sentence with incomplete moment and obtains
sub-optimal performance. Second, ViGA utilizes a fixed
scale Gaussian distribution centered at the glance frame to
describe the span of each annotated moment. However, the
glance annotations are not guaranteed at the center of target
moments, which results in annotation bias as shown in Fig-
ure 2. Besides, since some complex query sentences consist
of multiple events, a single Gaussian distribution is hard to
cover all events at the same time as shown in Figure 3. To
address the aforementioned defects and fully unleash the
potential of Gaussian prior knowledge with the low-cost
glance annotation, we propose a Dynamic Gaussian prior
based Grounding framework with Glance annotation (D3G)
as shown in Figure 4.
We first generate a wide range of candidate moments fol-
lowing 2D-TAN [43]. Afterwards, we propose a Semantic
Alignment Group Contrastive Learning module (SA-GCL)
to align the positive sentence-moment pairs in the joint em-
bedding space. Specifically, for each query sentence, we
sample a group of positive moments according to calibrated
Gausssian prior and minimize the distances between these
moments and the query sentence. In this way, it tends to
gradually mine the moments which have increasing over-
lap with the ground truth. Moreover, we propose a Dy-
namic Gaussian prior Adjustment module (DGA), which
further alleviates annotation bias and approximates the span
of complex moments consisting of multiple events. Specif-
ically, we adopt multiple Gaussian distributions to describe
the weight distributions of moments. Therefore, the weight
distributions for various moments can be flexibly adjusted
and gradually approach to the ground truth. Our contribu-
tions are summarized as follows:
• We propose a Dynamic Gaussian prior based Ground-
ing framework with Glance annotation (D3G), which
facilitates the development of temporal sentence
grounding with lower annotated cost.
• We propose a Semantic Alignment Group Contrastive
Learning module to align the features of the positive
sentence-moment pairs and a Dynamic Gaussian prior
Adjustment module to ease the annotation bias and
model the distributions of complex moments.
• Extensive experiments demonstrate that D3G obtains
consistent and significant gains compared to method
under the same annotating paradigm and outperforms
weakly supervised methods by a large margin.
𝑤!
𝑤"
𝑤#
𝑤!
,
𝑤"
,
𝑤#
,
target moment
target moment
𝑔
𝑔
Figure 2. Illustration of annotation bias and Gaussian prior after
dynamic adjustment. Top: the target moment is assigned with a
low weight wm due to the bias of glance annotation according to
ViGA, which we call annotation bias. Bottom: a reasonable Gaus-
sian distribution is obtained via DGA described in Section 3.3.
2. Related Work
Full Supervised Temporal Sentence Grounding. The FS-
TSG methods can be categorized into two groups. Two-
stage methods [1, 9, 11, 13, 14, 35] first propose candi-
date segments in a video through sliding window or pro-
posal generation. A cross-modal matching network is then
employed to find the best matching clip. However, these
propose-and-match paradigms are time-consuming due to
the numerous candidates. To reduce the redundant com-
putation, some researchers proposed single-stage meth-
ods [2, 3, 40, 41, 30, 20, 43, 21, 39, 37]. 2D-TAN [43]
constructs 2D feature map to model the temporal relations
of video segment. Recently, Wang et al. [33] propose a Mu-
tual Matching Network based on 2D-TAN, and further im-
prove the performance via exploiting both intra- and inter-
video negative samples. Although fully supervised meth-
ods achieve satisfying performance, they are highly depen-
dent on accurate timestamp annotations. It is highly time-
consuming and laborious to obtain these annotations for
large-scale video-sentence pairs.
Weakly Supervised Temporal Sentence Grounding.
Specifically,
WS-TSG methods can be grouped into
reconstruction-based methods [8, 18, 26, 4] and multi-
instance learning (MIL) methods [19, 10, 5, 38, 12, 27].
SCN [18] employs a semantic completion network to re-
cover the masked words in the query sentence with the gen-
erated proposals, which provides feedback for facilitating
final predictions. To further exploit the negative samples in
MIL-based methods, CNM [44] and CPL [45] propose to
generate proposals with Gaussian functions and introduce
intra-video contrastive learning. WS-TSG methods indeed
advance with low annotation cost, however, there still re-
mains a large performance gap compared to FS-TSG meth-
ods due to the discrepancy between video-level annotations
and clip-level task.
Glance Supervised Temporal Sentence Grounding. Re-


…
…
A person walks on the roof of a climbing equipment
then he goes down the wall 
and reach the floor and walk forward.
g
Figure 3. Illustration of complex query consists of multiple events.
Note that g indicates the position of glance annotation. The ac-
cording Gaussian distribution (red curves) is hard to cover the
whole target moments. We utilize DGA module to mine multiple
latent Gaussian distributions (dashed line) to model such query.
cently, ViGA [6] proposes glance supervised TSG (GS-
TSG) task with a new annotating paradigm.
ViGA uti-
lizes a Gaussian function to model the relevance of differ-
ent clips with target moment and contrasts the clips with the
queries. Though ViGA achieves promising performance, it
still suffers from two limitations as mentioned in Introduc-
tion. Concurrently, Xu et al. [36] propose the similar task
called PS-VTG, and generate pseudo segment-level labels
based on language activation sequences. To better explore
the Gaussian prior for TSG task with glance annotation, we
propose a simple yet effective D3G, which achieves com-
petitive performance compared with both WS-TSG and FS-
TSG methods. Concurrent with our work, Ju et al. [15]
propose a robust partial-full union framework (PFU) and
achieve excellent performance with glance annotation or
short-clip labels.
3. Proposed Method
3.1. Overview
Given an untrimmed video V and query sentence S, the
temporal sentence grounding task aims to determine the
start timestamp ts and end timestamp te, where the mo-
ment Vts:te best semantically corresponds to the query. As
for FS-TSG, the exact timestamps (ts,te) of corresponding
moment is provided given a query description. In contrast,
Cui et al. [6] propose a new low-cost annotating paradigm
called glance annotation, which requires only single times-
tamp g, satisfying g ∈[ts, te]. Following the setting of [6],
we propose a Dynamic Gaussian prior based Grounding
framework with Glance annotation (D3G) to fully unleash
the potential of glance annotations.
Our D3G adopts the network architecture similar to
[43, 33].
Given an untrimmed video, we firstly encode
the video into feature vectors with pre-trained 2D or 3D
convolutional network [25, 28] and segment the video fea-
tures into N video clips. Specifically, we apply average
pooling to each clip to obtain clip-level features V
=
{f v
1 , f v
2 , ..., f v
N} ∈RN×Dv. These clip features are then
passed through an FC layer to reduce their dimension, de-
noted as F 1d ∈RN×dv. Afterwards, we encode them as
2D temporal feature map ˆ
F ∈RN×N×dv following 2D-
TAN [43] with the max pooling. As for language encoder,
we choose DistilBERT [23] to obtain sentence-level feature
ˆ
f s ∈Rds following [33]. Finally, to estimate the matching
scores of candidate moments and the query, we utilize a lin-
ear projection layer to project the textual and visual features
into same dimension d, respectively. The final representa-
tion of sentence is f s ∈Rd and the features of all moments
are F ∈RN×N×d. The final matching scores are given by
the cosine similarity between f s and elements of F.
3.2. Semantic Alignment Group Contrastive Learn-
ing
In this section, we aim to mine the moment which most
semantically corresponds to the query and maximize the
similarity between them.
To achieve this goal, we have
two crucial steps. First, we generate abundant candidate
moments following 2D-TAN and assign them with reli-
able Gaussian prior weights generated with the guidance of
glance annotation. Second, we propose a semantic align-
ment group contrastive learning to align a group of positive
moments with corresponding query sentence.
To be specific, given the encoded video features F 1d ∈
RN×dv and glance annotation g, we also utilize a Gaussian
function parameterized with (µ, σ) to model the relations
between frames and target moment, where the µ is deter-
mined by the glance g. We first scale the sequence indices
I ∈{1, 2, ..., N} into domain [−1, 1] by a linear transfor-
mation as follows:
h(i) = 2· i −1
N −1 −1.
(1)
Given the index i, we can obtain corresponding Gaussian
weight via Eq. (2).
G(i, µ, σ) = Norm(
1
√
2πσ exp(−(h(i) −h(µ))2
2σ2
)), (2)
where µ ∈I and σ is a hyperparameter, and Norm(·) is a
function used to scale values into range [0, 1].
Different from ViGA [6], we utilize the characteristic
of 2D-TAN to generate a wide range of candidate mo-
ments with various durations.
Given the video features
F 1d ∈RN×dv, we encode them into 2D feature map
F ∈RN×N×d as shown in Figure 4, where Fij denotes
the feature of moment that starts at position i and ends at
position j. Note that the moment is valid only when i ≤j.
We then propose a triplet-sample strategy to generate more
reasonable weights for candidate moments instead of only
sampling the weight at middle point as in [6]. Specifically,


𝑤!"
update
pull
push
intra-video positive
SA-GCL
DGA
Video
feature 
extractor
max pooling
& conv & proj
Query: person pours 
some water into a glass.
DistillBERT
intra-video negative
inter-video negative
text feature
𝐹∈𝑅#×#×%
𝑓& ∈𝑅%
𝐹'% ∈𝑅#×%!
𝑓&
𝐹(
𝐹)
proj
𝑤!"
𝑠!"
𝑔
Figure 4. The overview of proposed D3G, which consists of Semantic Alignment Group Contrastive Learning (SA-GCL) and Dynamic
Gaussain prior Adjustment (DGA). Note that g indicates the position of glance annotation and the grids with dashed line in F are invalid
candidate moments. “proj” denotes the linear projection layer. “intra/inter” indicate the positive or negative moments sampled from
same/different videos.
for each moment with start position i and end position j, we
compute its Gaussian prior weight as follows:
wij = 1
3· (G(i, g, σ)+G(j, g, σ)+G(⌊i + j
2
⌋, g, σ)), (3)
where g is glance annotation for current target moment. In
this way, those moments containing target moment but hav-
ing longer durations will be penalized with lower weights.
To remedy the annotation bias, we additionally intro-
duce semantic consistency prior to calibrate the Gaussian
prior weight wij for each candidate moment. Given the
query features f s ∈Rd and the features F ∈RN×N×d of
candidate moments, we compute their semantic consistency
scores via Eq. (4).
sij =
f s · Fij
∥f s ∥∥Fij ∥,
(4)
where ∥· ∥is l2-norm. Afterwards, we rectify the Gaussian
weight wij with semantic consistency score sij via multi-
plication to obtain new prior weight pij = wij· sij.
The objective of Temporal Sentence Grounding is to
learn a cross-modal embedding space, where the query sen-
tence feature should be well aligned with the feature of
corresponding moment and far way from those of irrele-
vant video moments. Motivated by [31, 17], we propose
a Semantic Alignment Group Contrastive Learning module
(SA-GCL) to gradually mine candidate moments most se-
mantically aligned with given query sentence. To be spe-
cific, we first sample top-k candidate moments from F as
positive keys for query f s according to the new prior pij,
denoted as F p = {Fij|1 ≤i ≤j ≤N} ∈Rk×d. Simulta-
neously, we sample Gaussian weights of corresponding mo-
ments denoted as W p = {wij|1 ≤i ≤j ≤N} ∈Rk. We
then gather other candidate moments which do not contain
the glance g from intra-video and all candidate moments
from other videos within same batch as negative keys, de-
noted as F n = {Fij|1 ≤i ≤j ≤N} ∈RNn×d, where Nn
denotes the number of negative moments. The objective of
SA-GCL can be described as follows:
Lalign = −1
k
k
X
z=0
W p
z log exp(f s · F p
z /τ)
SUM
,
SUM =
k
X
z=0
exp(f s · F p
z /τ) +
Nn
X
z=0
exp(f s · F n
z /τ),
(5)
where τ is the temperature scaling factor. SA-GCL aims to
maximize the similarity between the query f s and a group
of corresponding positive moments F p under the joint em-
bedding space while pushing away negative pairs. Note that


different positive moments are assigned with corresponding
prior weight W p
z . In this way, SA-GCL effectively avoids
being dominated by inaccurate moments with less similar-
ity and tends to mine the candidate moments having large
overlap with the target moment.
3.3. Dynamic Gaussian prior Adjustment
To further ease the annotation bias and characterize com-
plex target moments, we propose a novel Dynamic Gaus-
sian prior Adjustment module (DGA). Specifically, we uti-
lize multiple Gaussian functions with different centers to
model the local distributions of target moment and aggre-
gate them to approximate the distribution of target moment.
Given the video features F 1d ∈RN×dv and annotation
glance g, we compute the relevance of other position i with
position g via Eq. (6).
rgi =
F 1d
g
· F 1d
i
∥F 1d
g
∥∥F 1d
i
∥.
(6)
¯
rgi = (1 −α)¯
rgi + αrgi.
(7)
To make the relevance scores more stable, we update
¯
rgi with momentum factor α as shown in Eq. (7), where
¯
rgi = rgi at first training epoch. According to the relevance
{¯
rgi}, we can mine latent local centers for target moment.
Specifically, we utilize a specific threshold Tr to filter the
candidate positions and obtain a mask Mg ∈{0, 1}N for
glance g as follows:
M i
g =
(
1,
if ¯
rgi ≥Tr
0,
otherwise
(8)
With the mask of latent local centers, we then adjust the
Gaussian prior dynamically via Eq. (9).
ˆ
G(i, g, σ) = 1
C
N
X
z=1
M z
g · ¯
rgi · G(i, z, σ),
(9)
where C is the summation of mask Mg. Afterwards, we
replace the G(i, g, σ) in Eq. (3) with ˆ
G(i, g, σ), and nat-
urally obtain dynamic Gaussian prior weight during train-
ing. Compared to ViGA, our dynamic Gaussian prior is
more flexible and able to adjust the center of Gaussian dis-
tribution adaptively. Therefore, DGA further alleviates the
annotation bias and provides more reliable prior weights.
Besides, multiple Gaussian distributions are well suited for
modeling complex target moments consisting of multiple
events as shown in Figure 3. DGA tends to widen the re-
gion of high Gaussian weight via self-mining neighboring
frames based on the feature of glance g and gradually gen-
erates the Gaussian prior weight well aligned with target
moment. In this way, SA-GCL will be provided with pos-
itive moments of high quality, which eventually promotes
the cross-modal semantic alignment learning and accurate
localization of target moments.
Discussion. To clearly distinguish the differences between
D3G and few similar works, we give some explanations
here. As for MMN, D3G shares the same process of gener-
ating candidate moments following 2D-TAN, which is not
the key contribution of our method.
MMN utilizes nor-
mal one-to-one contrastive learning is no longer suitable
to glance annotation. However, D3G instead adopts a suit-
able sample strategy and corresponding adapted group con-
trastive learning, which is key component to unleash the po-
tential of glance annotations. As for CPL, we also know that
it utilizes multiple Gaussian distribution to describe positive
moments. However, it actually select one most matched
positive moment guided by the loss of masked language
reconstruction for contrastive learning, while D3G utilizes
multiple Gaussian functions to adaptively model complex
queries consisting of multiple events and samples a group
of positive moments for contrastive learning.
4. Experiments
In order to validate the effectiveness of the proposed
D3G, we conduct extensive experiments on three publicly
available datasets: Charades-STA [9], TACoS [9] and Ac-
tivityNet Captions [16].
4.1. Datasets
Charades-STA is built on dataset Charades [24] for tem-
poral sentence grounding.
It contains 12,408 and 3,720
moment-sentence pairs for training and testing.
TACoS consists of 127 videos selected from the MPII
Cooking Composite Activities video corpus [22]. We fol-
low the standard split from [9], which contains 10,146,
4,589 and 4,083 moment-sentence pairs for training, vali-
dation and testing, respectively. We report the evaluation
result on the test set for fair comparison.
ActivityNet Captions is originally designed for video cap-
tioning and recently introduced into temporal sentence
grounding. It contains 37,417, 17,505 and 17,031 moment-
sentence pairs for training, validation and testing, respec-
tively. We report the evaluation result following [43, 33].
Specially, we adopt the glance annotation released by [6]
for training set, where the temporal boundary is replaced
with the timestamp g uniformly sampled within the original
temporal boundary.
4.2. Evaluation Metric and Implementation Details
Evaluation Metric.
Following previous works [9, 43],
we evaluate our model with metric ‘R@n,IoU=m’, which
means the percentage of at least one of the top-n results
having Intersection over Union (IoU) larger than m. Specif-
ically, we report the results with m
∈
{0.5, 0.7} for


Charades-STA, m ∈{0.3, 0.5, 0.7} for TACoS and Activi-
tyNet Captions, and n ∈{1, 5} for all datasets.
Implementation Details. In this work, our main frame-
work is extended from MMN [33] and most of experiment
settings keep the same. For fair comparison, following [33],
we adopt off-the-shelf video features for all datasets (VGG
feature for Charades and C3D feature for TACoS and Ac-
tivityNet Captions). Specifically, the dimension of joint fea-
ture space d is set to 256 and τ is set to 0.1. In SA-GCL, we
set the k as 10, 20 and 20 for Charades, TACoS and Activ-
ityNet Captions, respectively. The σ in Eq. (2) is set to 0.3,
0.2 and 0.6 for Charades, TACoS and ActivityNet Captions.
In DGA, Tr and α is set as 0.9 and 0.7, respectively.
4.3. Comparisons with the State-Of-The-Art
In order to provide comprehensive analysis, we com-
pare the proposed D3G with both fully/weakly/glance su-
pervised methods. As shown in Table 1, Table 2 and Ta-
ble 3, D3G achieves highly competitive results on three
datasets under glance supervision, and achieves compara-
ble performance compared with fully supervised methods.
Note that we highlight the best value for each setting re-
spectively. Based on the experimental results, we can draw
the following conclusions:
(1) Glance annotation provides more potential to achieve
better performance for temporal sentence grounding with
lower annotation cost. Although it is not entirely fair to
directly compare D3G with other weakly supervised meth-
ods due to introducing extra supervision, D3G significantly
exceeds most of weakly supervised methods by a large mar-
gin with trivial increment of annotation cost. Since PS-VTG
and PFU adopt more robust I3D feature, they obviously out-
perform D3G on Charades-STA. However, D3G instead is
superior to PS-VTG on more challenging TACoS with same
features. Besides, weak supervised methods are often not
tested on TACoS, where the videos are very long and con-
tain a large number of target moments. However, D3G ob-
tains promising performance and outperforms ViGA by a
large margin on TACoS as shown in Table 2.
(2) D3G effectively exploits the information provided by
glance annotation and mines more moments of high qual-
ity for training compared with ViGA. Due to the limitations
of fixed scale Gaussian function and fixed sliding window,
ViGA fails to mine accurate candidate moments to learn
a well-aligned joint embedding space. Instead, D3G gen-
erates a wide range of candidate moments and samples a
group of reliable candidate moments for group contrastive
learning. Compared to ViGA, D3G achieves obvious gains
5.08% and 3.5% at R@1 IoU=0.5 and R@1 IoU=0.7 on
Charades-STA, respectively. Specially, significant improve-
ments are obtained at R@5 on three datasets.
(3) D3G substantially narrows the performance gap be-
tween weakly/glance supervised methods and fully super-
Method
R@1
R@5
IoU=0.5
IoU=0.7
IoU=0.5
IoU=0.7
MAN [41]
41.21
20.54
83.21
51.85
2D-TAN [43]
39.70
23.31
80.32
51.26
SSCS [7]
43.15
25.54
84.26
54.17
MMN [33]
47.31
27.28
83.74
58.41
CRM [12]
34.76
16.37
-
-
CNM [44]
35.43
15.45
-
-
LCNet [38]
39.19
18.87
80.56
45.24
CPL† [45]
32.27
14.22
78.34
43.45
PS-VTG‡ [36]
39.22
20.17
-
-
PFU‡ [15]
54.66
28.34
-
-
VIGA∗[6]
36.56
16.10
48.90
25.86
D3G
41.64
19.60
79.25
49.30
Table 1. Performance comparison on Charades-STA under differ-
ent supervision settings.Top:full supervision, Middle: weak super-
vision, Bottom:glance supervision. †we reproduce the results with
official code and VGG features for fair comparison.
∗we repro-
duce the results with official code for results at R@5.‡ indicates
the method utilizes I3D features.
Method
R@1
R@5
IoU=0.3
IoU=0.5
IoU=0.7
IoU=0.3
IoU=0.5
IoU=0.7
CTRL [9]
18.32
13.30
-
36.69
25.42
-
2D-TAN [43]
37.29
25.32
-
57.81
24.04
-
SSCS [7]
41.33
29.56
-
60.65
48.01
-
MMN [33]
38.57
27.24
-
65.31
50.69
-
MAT [42]
48.79
37.57
-
67.63
57.91
-
VIGA∗[6]
20.82
9.52
3.10
27.92
15.35
6.10
PS-VTG [36]
23.64
10.00
3.35
-
-
-
D3G
27.27
12.67
4.70
54.61
31.34
12.35
Table 2. Performance comparison on TACoS under different super-
vision settings.Top:full supervision, Bottom:glance supervision.
∗we reproduce the results with official code for results at R@5.
vised methods. Specifically, D3G already surpasses previ-
ous method (e.g., CTRL) on both TACoS and ActivityNet
Captions. Undeniably, there are still non-negligible margin
compared to the state-of-the-art fully supervised methods
(e.g., MMN). Note that D3G is very concise and not embed-
ded with auxiliary module (e.g., MLM used in [45]). D3G
still can be enhanced with some complementary modules.
4.4. Ablation Study
To validate the effectiveness of different components of
the proposed D3G and investigate the impact of hyper-
parameters, we perform ablation studies on Charades-STA.
Effectiveness of SA-GCL and DGA. Since Lalign is the
only loss of D3G, to validate the effectiveness of SA-GCL,
we need to simplify the SA-GCL module as a baseline.
Specifically, we only sample the top-1 positive moment to


(a) Effect of top-k.
(b) Effect of σ.
(c) Effect of Tr
Figure 5. Effect of different hyper-parameters on Charades-STA dataset.
Method
R@1
R@5
IoU=0.3
IoU=0.5
IoU=0.7
IoU=0.3
IoU=0.5
IoU=0.7
CTRL [9]
47.43
29.01
10.34
75.32
59.17
37.54
2D-TAN [43]
59.46
44.51
26.54
85.53
77.13
61.96
LGI [20]
58.52
41.51
23.07
-
-
-
SSCS [7]
61.35
46.67
27.56
86.89
78.37
63.78
MMN [33]
65.05
48.59
29.26
87.25
79.50
64.76
MAT [42]
-
48.02
31.78
-
78.02
63.18
CRM [12]
55.26
32.19
-
-
-
-
CNM [44]
55.68
33.33
-
-
-
-
LCNet [38]
48.49
26.33
-
82.51
62.66
-
CPL [45]
53.67
31.24
-
63.05
43.14
-
VIGA∗[6]
59.78
35.39
16.25
72.19
53.19
32.69
PS-VTG [36]
59.71
39.59
21.98
-
-
-
PFU [15]
59.63
36.35
16.61
-
-
-
D3G
58.25
36.68
18.54
87.84
74.21
52.47
Table 3. Performance comparison on ActivityNet Captions under
different supervision settings.Top:full supervision, Middle: weak
supervision, Bottom:glance supervision. ∗we reproduce the results
with official code for results at R@5.
compute the normal contrastive loss (degraded to simpli-
fied MMN) as shown in the first row of Table 4. However,
the top-1 moment tends to be the shortest moment and has
small overlap with target moment, which is decided by the
intrinsic characteristic of 2D-TAN. Therefore, the perfor-
mance of baseline is undoubtedly very poor, which demon-
strates that the main improvement of D3G is not brought
by the backbone of MMN. This phenomenon then encour-
ages us to sample a group of positive moments in SA-GCL.
With full SA-GCL, the model obtains notable performance
gains. Moreover, we introduce the DGA to alleviate annota-
tion bias and model some complex target moments consist-
ing of multiple events. After equipped with DGA, D3G and
simplified D3G achieve obvious performance improvement.
Impact of Sampling Strategy. In SA-GCL, sampling a
group of reliable positive moments is of great importance.
We investigate the impacts of two priors: Gaussian weight
and semantic consistency, respectively. As shown in the first
row of Table 5, we sample top-k positive moments accord-
Module
R@1
R@5
SA-GCL DGA IoU=0.5 IoU=0.7 IoU=0.5 IoU=0.7
✓†
5.08
0.81
14.78
3.36
✓†
✓
13.92
3.31
33.55
11.77
✓
40.51
16.10
74.41
43.31
✓
✓
41.64
19.60
79.25
49.30
Table 4. Effectiveness of SA-GCL and DGA in D3G on Charades-
STA. ✓† denotes an simplified implementation of SA-GCL.
Types
R@1
R@5
GW
SC
IoU=0.5
IoU=0.7
IoU=0.5
IoU=0.7
✓
38.09
16.10
66.53
36.51
✓
25.67
9.57
65.43
38.52
✓
✓
40.51
16.10
74.41
43.31
Table 5. Impact of different strategies used to sample positive mo-
ments for SA-GCL on Charades-STA. GW: Gaussian weight, SC:
semantic consistency.
ing to the Gaussian prior weight. An alternative scheme is
that we sample top-k positive moments according to the se-
mantic consistency scores between candidate moments and
query sentence. However, both of them obtain sub-optimal
performance. This is because Gaussian prior weight is not
always reliable due to the annotation bias and semantic con-
sistence scores are highly dependent on the stability of fea-
tures. Therefore, we finally fuse these two priors to obtain
relatively reliable prior. As shown in the third row of Ta-
ble 5, obvious performance gains are obtained after both of
them are utilized, which demonstrates that these two priors
indeed complement each others.
Effect of different hyper-parameters. As shown in Fig-
ure 5, we investigate three critical hyperparameters in D3G.
As verified in Table 4, sampling enough latent positive mo-
ments is beneficial to mining target moment for training. As
shown in Figure 5 (a), the performance gains increase ob-
viously as the k increases. However, it begins to decrease


Method
R@1
R@5
IoU=0.5
IoU=0.7
IoU=0.5
IoU=0.7
ViGA
36.56
16.10
48.90
25.86
D3G
41.64
19.60
79.25
49.30
ViGA+ 33.66(−2.90) 14.65(−1.45) 47.45(−1.45) 25.51(−0.35)
D3G+
40.19(−1.45) 19.62(+0.02) 78.90(−0.35) 49.41(+0.11)
Table 6. Performance comparison on Charades-STA with extreme
glance annotation.
+ indicates according method is trained with
extreme glance annotations.
after the k reaches a specific value. We argue that selecting
excessive positive moments tends to incorporate some false
positive moments and therefore degrades the performance.
We finally set the k to 10 for Charades-STA, which bal-
ances well the performance and computational cost. As for
hyperparameter σ, it essentially decides the width of Gaus-
sian distribution. A larger σ can well characterize the target
moment of longer duration and vice versa. We vary the σ
from 0.1 to 0.5, and observe that value 0.3 is relatively suit-
able for the Charades-STA dataset. As for hyperparameter
Tr in Eq. (8), it controls the degree of dynamic Gaussian
prior adjustment. We conduct experiments with relevance
thresholds around 0.9. A small threshold tends to introduce
interference while a large threshold fails to find the neigh-
bor frames with consistent semantic. As shown in Figure 5
(c), the moderate threshold 0.9 relatively balances the afore-
mentioned dilemma.
Tolerance to Extreme Glance Annotation. In order to
verify the ability of addressing extreme glance annotation,
we first generate extreme glance annotation, where only the
positions near the start/end timestamps will be sampled as
glance g. As shown in Table 6, both ViGA+ and D3G+
are confronted with the performance degradation at some
metrics(e.g., R@1 IoU=0.5). However, the performances of
D3G are relatively stable compared to ViGA, which demon-
strate that D3G indeed is able to alleviate annotation bias.
4.5. Qualitative Analysis
To clearly reveal the effectiveness of our method, we
visualize some qualitative examples from the test split of
Charades-STA dataset and ActivityNet Captions dataset. As
shown in Figure 6, the proposed D3G achieves more ac-
curate localization of target moment compared to ViGA.
Specifically, ViGA cannot well align the visual content
and semantic information and tend to be disturbed by ir-
relevant content, which may be caused by the annotation
bias. Instead, D3G utilizes SA-GCL and DGA to alleviate
the annotation bias, which enables D3G to well align the
query with the corresponding moment. Moreover, the DGA
adopts multiple Gaussian functions to model target mo-
ment, which is beneficial to representing the complete dis-
tribution of complex moments consisting of multiple events.
As shown in Figure 6 (b), D3G still effectively localizes the
15.56 s – 29.39 s
21.61 s – 34.58 s
Query: person starts playing games on a laptop.                  
duration: 34.58 s             
GT
ViGA
D3G
21.9 s – 34.58 s
(a)
Query: The men cook together while mixing in more 
ingredients and presenting it on a plate.
duration: 121.84 s       
GT
ViGA
D3G
77.37 s – 121.84 s
17.06 s – 102.35 s
68.53 s – 118.03 s
(b)
Figure 6. Qualitative examples of top-1 predictions. (a) and (b)
is from the Charades-STA dataset and the ActivityNet Captions
dataset, respectively.
GT indicates the ground truth temporal
boundary.
complex moments while ViGA misses the last events “rep-
resent it on a plate”. More qualitative examples will be pro-
vided in Supplementary Materials.
5. Conclusion
In this study, we investigate a recently proposed task,
Temporal Sentence Grounding with Glance Annotation.
Under this setting, we propose a Dynamic Gaussian
prior based Grounding framework with Glance annota-
tion, termed D3G. Specifically, D3G consists of a Semantic
Alignment Group Contrastive Learning module (SA-GCL)
and a Dynamic Gaussian prior Adjustment module (DGA).
SA-GCL aims to mine a wide range of positive moments
and align the positive sentence-moment pairs in the joint
embedding space. DGA effectively alleviates the annota-
tion bias and models complex query consisting of multiple
events via dynamically adjusting the Gaussian prior with
multiple Gaussian functions, promoting the precision of lo-
calization. Extensive experiments show that D3G signifi-
cantly narrows the performance gap between fully super-
vised methods and glance supervised methods. Without ex-
cessive interaction of visual-language, D3G provides a con-
cise framework and a fresh insight to the challenging tem-
poral sentence grounding under low-cost glance annotation.
Limitations. Although D3G achieves promising improve-
ments with glance annotations, it still has some limitations.
In this paper, the DAG adjusts Gaussian prior via the com-
bination of multiple fixed scale Gaussian functions. It fails
to scale down the Gaussian distribution to fit the small mo-
ments. It is expected to explore dynamic learnable Gaussian
functions to model moment of arbitrary duration in future
work. Besides, the sampling strategy for SA-GCL is still
not enough flexible to sample accurate positive moments.


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Appendix
A. Effectiveness of SA-GCL and DGA
To further analyze the effectiveness of SA-GCL and
DGA, we provide more detailed experimental results on
ActivityNet Captions and TACoS datasets as shown in Ta-
ble 7 and Table 8. Following the main manuscript, we re-
gard the simplified implementation of SA-GCL as a base-
line.
After being equipped with the complete SA-GCL,
our model achieves significant improvements on both Ac-
tivityNet Captions and TACoS. This phenomenon demon-
strates that sampling enough positive moments for con-
trastive learning is of great importance. Additionally, we
further incorporate the DGA module for alleviating the an-
notation bias and modeling complex target moments. Since
the ActivityNet Captions dataset has a large number of com-
plex query sentences consisting of multiple events, D3G
obtains notable performance gains on ActivityNet Cap-
tions(e.g. 9.03% at R@5 IoU=0.7). However, TACoS is still
challenging for D3G due to the dense distributions of target
moments.
Module
R@1
R@5
SA-GCL DGA IoU=0.5 IoU=0.7 IoU=0.5 IoU=0.7
✓†
0.83
0.28
1.78
0.58
✓
32.65
16.00
65.48
43.44
✓
✓
36.68
18.54
74.21
52.47
Table 7. Effectiveness of SA-GCL and DAG in D3G on Activi-
tyNet Captions. ✓† denotes an simplified implementation of SA-
GCL.
Module
R@1
R@5
SA-GCL DGA IoU=0.5 IoU=0.7 IoU=0.5 IoU=0.7
✓†
2.97
0.37
5.40
1.10
✓
11.95
4.20
29.07
10.30
✓
✓
12.67
4.70
31.34
12.35
Table 8. Effectiveness of SA-GCL and DAG in D3G on TACoS.
✓† denotes an simplified implementation of SA-GCL.
B. Effect of different hyper-parameters
In this section, we investigate the effect of two criti-
cal hyperparameters on ActivityNet Captions and TACoS
datasets. As shown in Figure 7 and Figure 8, we report
the changes in performance at four metrics. As for top-k,
the performance increases dramatically as the k increases.
However, the performance gradually achieves saturation af-
ter the k reaches 15. We finally select k = 20 for both Ac-
tivityNet Captions and TACoS. As for σ, the ActivityNet
Figure 7. Effect of top-k and σ on ActivityNet Captions dataset.
Captions dataset tends to prefer large values while small
values are more suitable for the TACoS dataset. This is be-
cause the former contains a large number of long target mo-
ments while the latter contains numerous short target mo-
ments. As shown in Figure 7 and Figure 8, we eventually
select σ = 0.6 and σ = 0.2 for ActivityNet Captions and
TACoS for optimal performance, respectively.
C. Qualitative Analysis
In this section, we provide more qualitative examples
from the test split of the Charades-STA dataset, ActivityNet
Captions dataset, and TACoS dataset. For each video, we
select two queries for analysis. As shown in Figure 9 (a),
D3G locates the target moment accurately while ViGA ig-
nores the reason at the front of the target moment, given
Query 1. However, D3G is inferior to ViGA in some cases
such as Query 2. As for complex queries in ActivityNet
Captions, D3G still localizes a moment with a large over-
lap with the target moment. Since sentence-level features
may lose some information about specific events, D3G can-
not perceive accurate boundaries for some complex queries,
such as Figure 9 (b) Query 2. It is expected to explore event-


Figure 8. Effect of top-k and σ on TACoS dataset.
level features for queries consisting of multiple events in
the future. TACoS is the most challenging dataset, where
the videos have long durations and contain a large number
of moment-sentence pairs. As shown in Figure 9 (c), we
observe that D3G fails to locate a simple query of short du-
ration from the long video, given Query 1. However, D3G
accurately locates the target moment of long duration given
Query 2. Note that D3G well attends to the number “the
last two” of the query while ViGA fails to attend to such in-
formation and locates irrelevant moments. As observed in
Figure 9, D3G is superior to ViGA, which is consistent with
the experimental results in the main manuscript. However,
D3G still has some limitations and needs to be improved in
the future.


Query 1: person laughing because they see something funny on the television.
0 s – 5.2 s
4.03 s – 14.1 s
0 s – 6.29 s
Query 2: a person in their dining room is running around.
1.3 s – 12.8 s
1.01 s – 11.07 s
1.57 s – 7.87 s
Query 1: A man and a woman are standing outside at a beach in the sand talking while the lady 
holds a brown paper bag in her hand and a man begins filming them.
duration: 213.42 s             
0 s – 44.82 s
0 s – 64.03 s
0 s – 53.35 s
Query 2: The teams begin to get extremely individual and add words and feathers to their   
masterpiece before the man and lady come around to judge them.
138.72 – 185.67 s
57.62 s – 164.33 s
120.05 s – 213.42 s
…
5.17 s – 10.03 s
0 s – 37.91 s
8.89 s – 26.66 s
Query 1:The person gets out a cutting board.
Query 2: The person cuts up the last two slices of pineapple.
duration: 379.11 s             
327.14 s – 372.11 s
185.77 s – 242.63 s
346.54 s – 376.15 s
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
(a)
(b)
(c)
duration: 25.17 s             
Figure 9. Qualitative examples of top-1 predictions. (a), (b) and (c) is from the Charades-STA dataset, the ActivityNet Captions and the
TACoS dataset, respectively. GT indicates the ground truth temporal boundary.


Probability Distribution Based Frame-supervised
Language-driven Action Localization
Shuo Yang
shuoyang@bit.edu.cn
Beijing Key Laboratory of Intelligent
Information Technology, Beijing
Institute of Technology
Guangdong Laboratory of Machine
Perception and Intelligent Computing,
Shenzhen MSU-BIT University
Zirui Shang
ziruishang@bit.edu.cn
Beijing Key Laboratory of Intelligent
Information Technology, Beijing
Institute of Technology
Xinxiao Wu∗
wuxinxiao@bit.edu.cn
Beijing Key Laboratory of Intelligent
Information Technology, Beijing
Institute of Technology
Guangdong Laboratory of Machine
Perception and Intelligent Computing,
Shenzhen MSU-BIT University
ABSTRACT
Frame-supervised language-driven action localization aims to lo-
calize action boundaries in untrimmed videos corresponding to the
input natural language query, with only a single frame annotation
within the target action in training. This task is challenging due to
the absence of complete and accurate annotation of action bound-
aries, hindering visual-language alignment and action boundary
prediction. To address this challenge, we propose a novel method
that introduces distribution functions to model both the probability
of action frame and that of boundary frame. Specifically, we assign
each video frame the probability of being the action frame based on
the estimated shape parameters of the distribution function, serv-
ing as a foreground pseudo-label that guides cross-modal feature
learning. Moreover, we model the probabilities of start frame and
end frame of the target action using different distribution functions,
and then estimate the probability of each action candidate being
a positive candidate based on its start and end boundaries, which
facilitates predicting action boundaries by exploring more positive
terms in training. Experiments on two benchmark datasets demon-
strate that our method outperforms existing methods, achieving a
gain of more than 10% of 𝑅1@𝜇≥0.5 on the challenging TACoS
dataset. These results emphasize the significance of generating
pseudo labels with appropriate probabilities via distribution func-
tions to address the challenge of frame-supervised language-driven
action localization. 1
CCS CONCEPTS
• Information systems →Novelty in information retrieval;
Video search.
∗corresponding author
1Codes could be found at github
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15.00 https://doi.org/10.1145/3581783.3612512 KEYWORDS language-driven action localization, video moment retrieval, distri- bution, frame-supervised. ACM Reference Format: Shuo Yang, Zirui Shang, and Xinxiao Wu. 2023. Probability Distribution Based Frame-supervised Language-driven Action Localization. In Proceed- ings of the 31st ACM International Conference on Multimedia (MM ’23), Octo- ber 29–November 3, 2023, Ottawa, ON, Canada. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3581783.3612512 1 INTRODUCTION Language-driven action localization has drawn increasing atten- tion in recent years, which aims to locate the action interval in an untrimmed video that is semantically relevant to a language query. This task, also known as video moment retrieval [7, 34, 39, 46] or temporal sentence grounding [16, 18, 44], is a fundamental prob- lem in video understanding and multi-modal information retrieval, which involves not only cross-modal alignment but also action boundary localization. It has been widely applied in various sce- narios, such as content-based video search and automatic video editing. Previous methods [1, 6, 11, 31, 37, 43] have achieved remarkable success in the fully-supervised setting that requires annotating both the start and end timestamps of the target action corresponding to a given language query, as shown in Figure 1 (a). However, the frame annotation paradigm is time-consuming because annotators need to review videos multiple times to accurately identify action bound- aries. Consequently, recent methods [13, 25, 33, 38, 50] explore the weakly-supervised setting where only language-video pair annota- tions are provided, resulting in less annotation burden but lower performance, as shown in Figure 1 (b). The frame-supervised set- ting, first proposed in [4, 42], uses single-frame annotation within the target action, achieving a good balance between annotation cost and performance, as shown in Figure 1 (c). However, incom- plete annotations still hinder visual-language alignment and action boundary prediction. In this paper, we propose a new method to model the probabilities of action frames and boundary frames by introducing distribution functions. By exploiting the temporal consistency between video frames and properties of probability distribution functions, we ex- tend annotated frames to other frames with different probabilities. This enables the generation of frame-wise pseudo-labels of action frames, which is useful for learning video-language alignment. We 5164 MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. Shuo Yang, Zirui Shang, and Xinxiao Wu Language query: person opens a refrigerator Language query: person opens a refrigerator Language query: person opens a refrigerator 10.5s 18.1s 12.6s (b) Weakly-supervised (c) Frame-supervised (a) Fully-supervised Figure 1: Illustration of different settings of language-driven action localization. Given an input language query and an input video, (a) the fully-supervised setting provides the start- ing and ending boundaries of the target action, (b) the weakly- supervised setting provides no additional labels, and (c) the frame-supervised setting gives a frame annotation within the target action. also model the probabilities of a video frame as the start and end of the target action via different distribution functions. And by multi- plying the start and end probabilities, we can obtain the probability of an action candidate being a positive target action segment. By doing so, all action candidates can be treated as positive action can- didates with varying probabilities, enabling the exploration of all possible positive action candidates with appropriate probabilities and thus improving the accuracy of boundary estimation. Specifically, we assign each video frame a probability of being the action frame, which is highest at the annotated frame and grad- ually decreases to a minimum near the boundary. To model this probability, we estimate the parameters of a specific distribution based on the visual similarity and temporal distance between the video frames and the annotated frame. In particular, we use the various asymmetrical probability curves of Beta distribution to han- dle the situations in which only few annotated frames are located at the center of the action segments. Using the resulting probabil- ity as a soft label for each action frame, we can optimize a binary cross-entropy loss that forces the visual feature to be similar to the language query feature with appropriate loss weights. Furthermore, we introduce another distribution function to model the probability of each video frame being the start or end bound- ary of the target action. In our method, the highest probabilities are assigned to the boundaries of an action candidate, or so-called proposal, that is closest to the language query in the feature embed- ding space, while the annotated frame is less likely to be an action boundary, as illustrated in Figure 4. We combine the probabilities of the starting and ending boundaries to give each action candidate a probability of being the target action segment. This helps us iden- tify more positive action candidates with reasonable probabilities, thereby facilitating the localization of action boundaries. The main contributions of this paper are as follows: • We propose a novel method that uses distribution functions, such as the Beta distribution, to generate a probability for each video frame being the action frame, serving as a pseudo- label to enhance the cross-modal feature learning. • We propose to use different distribution functions to model the probabilities of the start frame and end frame of the target action, so as to explore more positive action candidates during training, thus facilitating the localization of action boundaries. • Experiments on two benchmark datasets demonstrate that our method outperforms existing methods, especially achiev- ing a gain of more than 10% of 𝑅1@𝜇≥0.5 on the challeng- ing TACoS dataset. 2 RELATED WORK Current language-driven action localization settings can be roughly divided into three types: fully-supervised, weakly-supervised and frame-supervised. Fully-supervised language-driven action localization re- quires the annotation of start and end timestamps for each query during training. Existing methods of this setting can be broadly categorized into two groups: proposal-based and proposal-free. In the proposal-based methods, candidate proposals are first generated using sliding windows, proposal generation, or anchor-based meth- ods, and are then ranked based on queries. For instance, CTRL [6], MCN [1], MARN [19], HVSARN [20] and TSTNet [40] generate pro- posals of varying lengths through sliding windows. 2D-TAN [48], MGPN [32], HLN [5] and VDI [22] generate proposals by using a two-dimensional feature map that model the relationships be- tween segments of varying durations. The proposal-free methods directly predict the start and end boundaries of the target action on sequences of fine-grained video clips. According to the format of moment boundaries, proposal-free methods are categorized into span-based and regression-based methods. VSLNet [47], SLP [14] and D-TSG [17] directly predict the probability of each video snip- pet or frame being the start and end positions of the target action. TVP [49] and MGSL-Net [15] calculates a time pair and compares it with ground truth for model optimization. Weakly-supervised language-driven action localization only requires the annotation of pairs of video and query instead of the annotation of start and end times, thus reducing the high an- notation cost. Existing weakly-supervised language-driven action localization methods can be broadly classified into two categories: multi-instance learning and reconstruction-based methods. For in- stance, TGA [25] regards the video and its corresponding query de- scriptions as positive pairs, while considering the video with other queries and the query with other videos as negative pairs. This method learns video-level visual-text alignment by maximizing the matching scores of positive samples while minimizing the scores of negative samples. SAN [38] introduces a multi-scale Siamese module that progressively narrows the semantic gap between the visual and textual modalities. RTBPN [50] uses a language-aware filter to generate an enhanced video stream and a suppressed video stream, which are used to generate positive proposals and negative proposals for sufficient confrontment, separately. 5165 Distribution Based Frame-supervised Language-driven Action Localization MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. Frame-supervision is a setting that aims to strike a balance between annotation cost and performance, which has been applied to various computer vision tasks. Bearman et al. [2] introduce the concept of frame supervision for semantic segmentation. Mettes et al. [24] extend the use of frame supervision to spatio-temporal action localization in videos. In recent years, Ma et al. [23] pro- pose a SF-Net model for video temporal action localization by using single-frame supervision. Li et al. [12] develop a temporal action seg- mentation model that requires only timestamp annotations. More recently, some studies investigated the implementation of single- frame annotation for language-driven action localization. Cui et al. [4] originally introduce the concept of frame supervision for language-driven action localization, which uses the Gaussian distri- bution to model the probability distribution of foreground frames. Meanwhile, Xu et al. [42] employ a combination of Language Acti- vation Sequence (LAS) and given frame supervision to enhance the model’s ability in language-driven action localization. 3 OUR METHOD 3.1 Problem Definition Given an untrimmed video and a language query, the task of frame- supervised language-driven action localization aims to localize the target action boundaries (𝜏𝑠,𝜏𝑒) with an additional frame annota- tion 𝑡𝑝in the training stage, where 𝜏𝑠≤𝑡𝑝≤𝜏𝑒, and 𝜏𝑠and 𝜏𝑒 represent the start and end frames of the action corresponding to the language query, respectively. Note that in the inference stage, the frame annotation is not available. 3.2 Baseline Model Due to the lack of boundary annotation, we propose a baseline model of frame-supervised language-driven action localization, which follows an multiple-instance learning (MIL) strategy and consists of three components: a video encoder, a language encoder, and a cross-modal interaction module, as shown in Figure 2. Video Encoder. We first split the given video into a sequence of non-overlap clips with a fixed length (e.g., 16 frames) and ex- tract visual features of each clip by a pre-trained 3D-CNN [3, 35]. Then we uniformly sample 𝑇features and project them into 𝑑- dimensional representations using a fully-connected (FC) layer. Finally, we encode temporal relationships using a standard Trans- former block [36] that consists of multi-head self-attention, layer normalization, residual connection, and feed-forward network. The process of video encoding is represented by 𝑽′ = 𝑇𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑒𝑟(𝐹𝐶(Uniform-Sample(3D-CNN(𝑉)))). (1) Language Encoder. Given an input language query, we first ini- tialize its word features using the GloVe embedding [28] and then project their dimension to 𝑑by a fully-connected (FC) layer, fol- lowed by a three-layer bi-directional Gated Recurrent Unit (GRU) to learn the relationships of words: 𝑸′ = Bi-GRU(𝐹𝐶(𝐺𝑙𝑜𝑉𝑒(𝑄))). (2) Cross-modal Interaction Module. After encoding the video and language query, we adopt two cross-attention modules for the cross- modal interaction, each by regarding one modality as the query and the other as key and value, followed by a layer normalization, a residual connection and a feed-forward network: 𝑭𝑣= 𝐹𝐹𝑁(𝐿𝑁(𝑀𝑆𝐴(𝐹𝐶𝑄(𝑽′), 𝐹𝐶𝐾(𝑸′), 𝐹𝐶𝑉(𝑸′)) + 𝑽′)), 𝑭𝑞= 𝐹𝐹𝑁(𝐿𝑁(𝑀𝑆𝐴(𝐹𝐶𝑄(𝑸′), 𝐹𝐶𝐾(𝑽′), 𝐹𝐶𝑉(𝑽′)) + 𝑸′)), (3) where 𝑀𝑆𝐴is the multi-head self-attention module[36]; 𝐿𝑁de- notes layer normalization; 𝐹𝐶𝑗(·) denotes fully connected layer, 𝑗∈{𝑄, 𝐾,𝑉}; 𝐹𝐹𝑁(·) is a feed-forward network. Training loss. For the learned video features 𝑭𝑣, we generate ac- tion candidates 𝑃= {𝑃1, 𝑃2, · · · , 𝑃𝑁}, also known as proposals, by sliding windows, where 𝑁is the total number of action candidates, and 𝑃𝑖= max-pooling([𝑭𝑣,𝑠𝑖, · · · , 𝑭𝑣,𝑒𝑖]) ∈R𝑑is the 𝑖-th action candidate and 𝑠𝑖and 𝑒𝑖are its start and end frame index, respec- tively; And we also compute the sentence features by mean-pooling on the learned language features: 𝑭𝑠= mean-pooling(𝑭𝑞) ∈R𝑑. In the training stage, we use an intra-video loss and an inter- video loss to learn the video-language alignment. The intra-video loss treats the action candidates containing the annotated frame as positive candidates 𝑃+ and others as negatives 𝑃−. It enforces the similarities between the language query and positive action candidates larger than the similarities between the language query and negative candidates by the InfoNCE [27] loss, given by L𝑖𝑛𝑡𝑟𝑎= −1 𝑀 ∑︁ 𝑝𝑖∈𝑃+ log exp(𝑆(𝑝𝑖, 𝑭𝑠)/𝜏) exp(𝑆(𝑝𝑖, 𝑭𝑠)/𝜏) + ∑︁ 𝑝𝑗∈𝑃− exp(𝑆(𝑝𝑗, 𝑭𝑠)/𝜏) , (4) where 𝑆(·, ·) is the cosine similarity function; 𝑀is the number of positive action candidates; 𝜏is a temperature parameter and set to 0.07 as ViGA [4]. The inter-video loss is also an InfoNCE loss and calculated in a mini-batch, where the positive candidates 𝑃+ in the paired video- sentences are positive terms, and all action candidates of unpaired video-sentences are negative terms. The inter-video enforces the similarities between positive terms larger than the similarities be- tween negative terms in a mini-batch, given by L𝑖𝑛𝑡𝑒𝑟= − 1 𝑀×𝐵 𝐵 ∑︁ 𝑏=0 ∑︁ 𝑝𝑏,𝑖∈𝑃+ 𝑏 log exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠 𝑏)/𝜏) exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠 𝑏)/𝜏) + N , N = ∑︁ 𝑗≠𝑏 exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠 𝑗)/𝜏) + exp(𝑆(𝑝𝑗, 𝑭𝑠 𝑏)/𝜏) , (5) where 𝑝𝑏,𝑖∈𝑃+ 𝑏is the 𝑖-th positive candidates of 𝑏-th video in a mini-batch; 𝑀is the size of 𝑃+ 𝑏; 𝐵is batch size; N denotes the negative terms of none paired video-sentence in the mini-batch. 3.3 Pseudo-label of Action Frame The baseline model uses frame annotations to distinguish positive and negative action candidates, which ignores the temporal coher- ence of videos. Indeed, the annotated frames play a pivotal role in the frame-supervised language-driven action localization. A com- mon intuition is that the frames adjacent to the annotated frame are more likely to be action frames, while the frames far from the annotated frame are less likely to be action frames. However, how these possibilities change remains an open problem. In this study, we propose to use distribution functions to model the probability changes, taking into account temporal distance and visual similarity between video frames. The resulting probabilities 5166 MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. Shuo Yang, Zirui Shang, and Xinxiao Wu Fq Language query: person opens a refrigerator Video Encoder Language Encoder Cross-modal Interaction Fv Candidates …… A Probability of Action boundary s’ e’ start end Intra-loss Inter-loss Candidate labels Probability of Action frame A Action frame labels Fore-loss Fs Distribution based loss Baseline model Figure 2: Overview of the proposed method. In the distribution based loss, 𝐴denotes the annotated frame; 𝑠 ′ and 𝑒 ′ are the boundaries of the candidate with maximum similarity to the language query; the curves represent the probabilities by the estimated distributions, by which the pseudo labels of action frames and action candidates are generated (dark color means high probability). Probability of Action frame η 1 0 Probability of Action frame η 1 0 Probability of Action frame A τs τe η 1 0 T (a) Triangular (b) Gaussian (c) Beta A τs τe T A τs τe T Figure 3: Probabilities of target action frames by different distributions: (a) Triangular Distribution, (b) Gaussian Distri- bution, and (c) Beta Distribution. 𝜏𝑠and 𝜏𝑒denote the ground- truth boundary of target action; 𝐴is the annotated frame; 𝑇 is the video length; 𝜂is the minimum probability. can be viewed as pseudo-labels for action frames, which extends the annotated frame to its neighbors with different probabilities. This extension provides valuable guidance for learning cross-modal alignment, thereby improving the accuracy of action localization. By leveraging distribution functions in this way, we aim to improve the performance of action localization in videos. Here we explore three distributions to model the probability of frames being the action frames. Triangular distribution. We start with a simple distribution, the Triangular distribution, which models the probability changes based only on the temporal distance between frames. In this distribution, the probability decreases linearly with the increasing distance from the annotated frame. As shown in Figure 3(a), we assume that the minimum probability of vidoe frame is 𝜂, i.e., 𝜂= 1 𝑇, where 𝑇is the length of video, and the maximum probability of the annotated frame 𝐴is 1, the probability of the frame 𝑥being the action frame is calculated by 𝑃𝑓 𝑡(𝑥) = ( 1 𝐷( 𝑥(1−𝜂) 𝐴 + 𝜂), 0 ≤𝑥≤𝐴 1 𝐷( (𝑇−𝑥) (1−𝜂) (𝑇−𝐴) + 𝜂), 𝐴< 𝑥≤𝑇 (6) where the factor 1 𝐷is used as a normalization factor to ensure that the sum of all probabilities is equal to 1. However, in cases where we want the maximum probability to be 1, we can discard this normalization factor by setting 𝐷= 1 without affecting the shape of the distribution. Gaussian distribution. The Gaussian distribution can also model the probability changes of action frames. This more sophisticated distribution assumes that the probability changes follow a bell curve, with the highest probability occurring at the annotated frame and gradually decreasing as the distance from the annotated frame in- creases, as illustrated in Figure 3(b). The mean 𝜇of the Gaussian distribution is set to the annotated frame 𝐴, and we estimate its vari- ance 𝜎2 by considering both the temporal distance and visual simi- larity between frames. The temporal distance between the frame 𝑡 and the annotated frame 𝐴is 𝐷(𝑡,𝐴) = |(𝑡−𝐴)| 𝑇 . The visual similar- ity is calculated by𝑉(𝑡,𝐴) = 0.5×𝑐𝑜𝑠𝑖𝑛𝑒_𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑭𝑣𝑡, 𝑭𝑣𝐴)+0.5, and finally, the similarity is a weighted sum of temporal distance and visual similarity: 𝑆𝐼𝑀(𝑡,𝐴) = 𝜆1 · 𝑉(𝑡,𝐴) + 𝜆2 · (1 −𝐷(𝑡,𝐴)), (7) where 𝜆1 and 𝜆2 are hyper-parameters. The standard deviation 𝜎 can be estimated by 𝜎= (𝐶𝑙+ 𝐶𝑟)/2, where 𝐶𝑙and 𝐶𝑟denote the number of frames on the left side (𝑡< 𝐴) and right side (𝑡> 𝐴) of the annotated frame, respectively. These frames satisfy the condition 𝑆𝐼𝑀(𝑡,𝐴) ≥𝜃, where 𝜃is a hyper-parameter set to 0.9 times the maximum value of 𝑆𝐼𝑀(𝑡,𝐴). With 𝜇and 𝜎, the probability of the video frames 𝑥being the action frame is calculated by 𝑃𝑓 𝑔(𝑥) = exp(−(𝑥−𝜇)2 2𝜎2 ). (8) Since the annotated frame is not always located at the center of the action, the probability changes of the frames on either side of the annotated frame may not be symmetrical. However, the Gaussian distribution assumes that the changes are symmetrical, which limits its accuracy in certain situations. Beta distribution. To overcome the limitation of the Gaussian distribution, we introduce the Beta distribution. As shown in Fig- ure 3(c), the diverse curve and asymmetric probability of the Beta distribution enables modeling different relative positions of the annotated frame within an action. The probability density function 5167 Distribution Based Frame-supervised Language-driven Action Localization MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. Probability of boundary frame A s’ τe η 1 0 T (a) Triangular τs e’ Probability of boundary frame η 1 0 (c) Beta A τs τe T s’ e’ Probability of boundary frame A s’ τe η 1 0 T (b) Gaussian τs e’ Figure 4: Probabilities of start frames (red curves) and end frames (blue curves) of the target action by the (a) Triangular Distribution, (b) Gaussian Distribution, and (c) Beta Distri- bution. 𝑠 ′ and 𝑒 ′ denote the start and end boundaries of the selected action candidate, whose similarity with language query is the largest; of the Beta distribution is an exponential function of the variable 𝑥 and its reflection (1 −𝑥) as follows: 𝑓(𝑥;𝛼, 𝛽) = 𝑥𝛼−1(1 −𝑥)𝛽−1 B(𝛼, 𝛽) for 0 ≤𝑥≤1, (9) where 𝛼and 𝛽are the shape parameters, and B(𝛼, 𝛽) is the beta function serving as a normalization factor to ensure the total prob- ability is 1. The mean 𝜇and variance 𝜎2 are calculated by 𝜇= 𝛼 𝛼+ 𝛽, 𝜎2 = 𝛼𝛽 (𝛼+ 𝛽)2(𝛼+ 𝛽+ 1) . (10) As the work [41] says, it is hard to directly estimate the parameters 𝛼and 𝛽. Thus, we estimate the parameters of Beta Distribution by its mean and variance. To do so, we again calculate the similarity between the video frame and the annotated frame by Eq.(7), and set the similarity value larger than the threshold 𝜃to 1 and others to minimum probability 𝜂, same as Triangular distribution, we set 𝜂= 1 𝑇, where 𝑇is the length of video: 𝑆𝐼𝑀(𝑥,𝐴) ′ = ( 1, 𝑆𝐼𝑀(𝑥,𝐴) ≥𝜃 𝜂, 𝑜𝑡ℎ𝑒𝑟𝑠 (11) Then the mean ¯ 𝜇and variance ¯ 𝜎2 are calculated by ¯ 𝜇= 1 𝑇 𝑇 ∑︁ 𝑥=1 𝑆𝐼𝑀(𝑥,𝐴) ′ · 𝑥 𝑇, ¯ 𝜎2 = 1 𝑇 𝑇 ∑︁ 𝑥=1 𝑆𝐼𝑀(𝑥,𝐴) ′ · ( 𝑥 𝑇−𝜇)2. (12) Finally, the parameters of the Beta distribution are derived as 𝛼= ¯ 𝜇 ¯ 𝜇(1 −¯ 𝜇) ¯ 𝜎2 −1 , 𝛽= (1 −¯ 𝜇) ¯ 𝜇(1 −¯ 𝜇) ¯ 𝜎2 −1 (13) Therefore, the probability of the video frames 𝑥being the action frame is calculated by 𝑃𝑓 𝑏(𝑥) = 1 B(𝛼, 𝛽) 𝑥 𝑇 𝛼−1 1 −𝑥 𝑇 𝛽−1 . (14) In cases where we want the maximum probability to be 1, we replace the normalization factor B(𝛼, 𝛽) with a min-max normalization. In general, the Beta distribution is more complex to estimate than other distributions, which may limit its applications in some scenarios. Nonetheless, this distribution is a valuable tool for modeling the probability of action frames. Foreground Loss. With the probabilities of video frames being the action frames, we introduce a foreground loss to enforce the embedding of the relevant video frame close to the language query, which helps to learn cross-modal alignment, given by L𝑓𝑜𝑟𝑒= 𝐵𝐶𝐸(𝑐𝑜𝑠𝑖𝑛𝑒_𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑭𝑞, 𝑭𝑣)/𝜏, 𝑃𝑓 𝑑), (15) where 𝑭𝑞and 𝑭𝑣are the language features and video features, respectively; 𝐵𝐶𝐸is the binary cross entropy loss; 𝜏is the tempera- ture parameter; 𝑃𝑓 𝑑with 𝑑∈{𝑡,𝑔,𝑏} are the probabilities of action frames computed by the Triangular, Gaussian, or Beta distributions. 3.4 Pseudo-label of Boundary Frame In the baseline model of frame-supervised language-driven action localization, all action candidates containing the annotated frame are treated as positive, and others are negative, which may intro- duce significant noise. As such, we propose to use distribution functions to model the probability of each video frame being a po- tential starting or ending boundary of the target action moment. By modeling the temporal boundaries in this manner, each action can- didate is assigned a probability of being the target action moment by multiplying the probabilities of its boundaries. Given that the annotated frame is inclined to be within the target action, it is expected to have a minimum probability as a boundary. And for the positive action candidates 𝑃+ that include the annotated frame, we designate the one with the maximum cosine similarity to the language query as the pseudo target action segment, whose staring and ending boundaries are denoted as 𝑠′ and 𝑒′ in Figure 4, and assign it a maximum probability of 1. We formulate the starting boundary probability within the interval [0,𝐴] and the ending boundary probability within the interval [𝐴,𝑇]. As discussed in Section 3.3, the starting probability 𝑃𝑠 𝑑and the ending probability 𝑃𝑒 𝑑are modeled using distribution functions, and details are omitted here. Consequently, the probability of an action candidate being positive (i.e., belonging to the target action) is computed by taking the multiplication of its boundary probabilities,given by 𝑃𝑝 𝑑(𝑖) = 𝑃𝑝 𝑑(𝑥1,𝑥2) = 𝑃𝑠 𝑑(𝑥1) × 𝑃𝑒 𝑑(𝑥2), 𝑑∈{𝑡,𝑔,𝑏}, (16) where 𝑥1 and 𝑥2 are the starting and ending frame indexes of the 𝑖-th action candidate, and 𝑑denotes the index of distributions. As we assign each action candidate a probability by Eq.(16), we update the loss function in the baseline model. Specifically, we use the 𝑃𝑝 𝑑(𝑖) as the loss weight, and accordingly the intra-video and inter-video losses are re-written as L ′ 𝑖𝑛𝑡𝑟𝑎= −1 𝑀 ∑︁ 𝑝𝑖∈𝑃+ 𝑃𝑝 𝑑(𝑖) · log exp(𝑆(𝑝𝑖, 𝑭𝑠)/𝜏) exp( 𝑆(𝑝𝑖,𝑭𝑠) 𝜏 ) + ∑︁ 𝑝𝑗∈𝑃− exp( 𝑆(𝑝𝑗, 𝑭𝑠) 𝜏 ) , (17) L ′ 𝑖𝑛𝑡𝑒𝑟= − 1 𝑀· 𝐵 𝐵 ∑︁ 𝑏=0 ∑︁ 𝑝𝑏,𝑖∈𝑃+ 𝑏 𝑃𝑝 𝑑(𝑖) · log exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠 𝑏)/𝜏) exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠 𝑏)/𝜏) + N . (18) 3.5 Inference In the inference stage, we first compute the cosine similarity be- tween the video features and language features and then filter out 5168 MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. Shuo Yang, Zirui Shang, and Xinxiao Wu the action candidates that do not contain the top-k frames based on their similarity scores. After filtering, we rank all the remaining action candidates to determine the most likely one. This method allows us to efficiently identify the action in the video correspond- ing to the language query by considering both frame and segment features. 4 EXPERIMENTS 4.1 Datasets To evaluate the proposed method, we conduct experiments on two benchmark datasets, including the TACoS and the Charades-STA datasets. The TACoS dataset is built on the MPII Cooking Compositive dataset [29], which consists of 127 videos with an average length of 4.79 minutes. There are around 148 annotated segments per video. The dataset contains 18,818 samples, including 10,146 for training, 4,589 for validation, and 4,083 for testing. This dataset is more challenging due to the long videos and short action segments. The Charades-STA dataset is built on the Charades dataset [30] and contains 6,672 daily life videos. The average duration of the videos is 29.76 seconds. There are about 2.4 annotated segments per video, whose average duration is 8.2 seconds. The whole dataset contains 16,128 samples (i.e., pairs of query and action segment), and we follow the standard split of 12,408 and 3,720 samples for training and testing. 4.2 Evaluation Metrics We adopt two metrics for the performance evaluation: (1)𝑅@𝑛; 𝐼𝑜𝑈≥ 𝜇, which denotes the recall of top-𝑛predictions at various thresh- olds of the temporal Intersection over Union (IoU). It measures the percentage of predictions that have IoU with ground truth larger than the threshold 𝜇; (2) mean averaged IoU (mIoU), which de- notes the average IoU over all the test samples. We set 𝑛= 1 and 𝜇∈{0.3, 0.5, 0.7}. 4.3 Implementation Details We use C3D [35] for the TACoS dataset and I3D [3] for the Charades- STA dataset to extract video features. Adam [10] is adopted for optimization with an initial learning rate of 1e-4 and half decaying on plateau. The intermediate feature dimension 𝑑is set to 512, and the head number of multi-head self-attention is set to 8. The hyper- parameters 𝜆1 and 𝜆2 in Eq.(7) are set to 0.2 and 1 for Charades-STA, and 0.6 and 0.8 for TACoS. The loss weights for all loss items are set to 1. 4.4 Ablation Studies We perform in-depth analysis to evaluate each component of our method on the TACoS and Charades-STA datasets. Effectiveness of different distributions. We perform an abla- tion study using the Triangular, Gaussian, and Beta distributions to demonstrate the effectiveness of incorporating different distri- butions into the baseline model. The results on the TACoS and Charades-STA datasets are shown in Table 1 and Table 2, respec- tively. It is obvious that each of these distributions significantly improves the performance of the baseline model on the TACoS Table 1: Ablation studies of different distributions on the TACoS dataset. Methods 𝑅@1; 𝐼𝑜𝑈≥𝜇 𝑚𝐼𝑜𝑈 0.3 0.5 0.7 Baseline 17.05 6.45 1.87 15.47 Ours (Triangular) 34.64 19.02 6.47 22.23 Ours (Gaussian) 35.87 19.47 6.95 22.85 Ours (Beta) 36.14 20.17 7.30 23.09 Table 2: Ablation studies of different distributions on the Charades-STA dataset. Methods 𝑅@1; 𝐼𝑜𝑈≥𝜇 𝑚𝐼𝑜𝑈 0.3 0.5 0.7 Baseline 70.4 45.05 20.03 44.30 Ours (Triangular) 66.94 42.63 19.22 42.67 Ours (Gaussian) 71.10 48.15 25.65 46.75 Ours (Beta) 71.72 50.13 26.72 47.35 dataset, and similar trends can be seen on the Charades-STA dataset except for the Triangular distribution, where the Triangular distri- bution gives small probabilities to the frame near the ground-truth due to the short videos. Specifically, on the TACoS dataset, the Triangular distribution improves the 𝑅@1; 𝐼𝑜𝑈≥0.3 by 17.59%, the Gaussian distribution improves the 𝑅@1; 𝐼𝑜𝑈≥0.3 by 18.82%, and the Beta distribution achieves the greatest improvement with the gain of 19.09% on the 𝑅@1; 𝐼𝑜𝑈≥0.3. These results demonstrate that the pseudo-labels with appropriate probabilities provide pos- itive guidance for learning cross-modal alignment and boundary estimation. Effectiveness of the Beta distribution. The asymmetric nature of the Beta distribution makes it well-suited to handle the annotated frames that occur near the action boundaries. To evaluate the effec- tiveness of the Beta distribution, we conduct experiments where the training videos are re-annotated by placing annotated frames at various positions within the video. For example, the annotated frames are at the first ten percent of the action segment, which we denote as 0.1 in Table 3. We re-implement ViGA [4] by re-training the model using the new annotated frames via the open-source codes for comparison with our method. The comparison results are reported in Table 3. Our results show consistent improvements in 𝑅@1; 𝐼𝑜𝑈≥0.5 and 𝑅@1; 𝐼𝑜𝑈≥0.7, regardless of whether the annotation is located in the center of the action (represented as 0.5 in Table 3) or near the action boundaries (represented as 0.1 and 0.9 in Table 3). However, we observed fewer improvements when the annotations were near the boundaries, such as at ten (0.1) and ninety (0.9) percentages of the action segment. This can be attributed to the difficulty in estimating the distribution parameters accurately in such situations. We also noticed a slight decrease (less than 1%) in performance on 𝑅@1; 𝐼𝑜𝑈≥0.3, which we believe may be due to the neglect of some information when the probabilities for certain video frames are lower. Nonetheless, we are encouraged by the overall effectiveness 5169 Distribution Based Frame-supervised Language-driven Action Localization MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. Table 3: Ablation studies of different annotation positions 𝑃 on the Charades dataset. The value format 𝑎/𝑏in the table denotes that 𝑎is the result of the re-trained ViGA [4] and 𝑏 is the result of our method. P 𝑅@1; 𝐼𝑜𝑈≥𝜇 𝑚𝐼𝑜𝑈 0.3 0.5 0.7 0.1 65.59/65.05 41.42/43.39 20.13/21.64 42.44/42.80 0.3 69.25/70.13 46.05/48.60 20.73/25.38 44.19/46.46 0.5 71.88/71.53 44.97/47.98 21.26/23.28 45.01/45.93 0.7 68.39/68.09 44.41/45.24 19.41/21.34 43.12/43.77 0.9 61.77/61.05 36.29/36.99 15.32/16.75 38.75/38.90 Table 4: Ablation studies of different components on the Charades-STA dataset. “r1i3” is the short of 𝑅@1; 𝐼𝑜𝑈≥0.3. “v- s” is the short of visual similarity. “intra” and “inte” represent the intra-video loss and the inter-video loss, respectively. v-s intra inter r1i3 r1i5 r1i7 𝑚𝐼𝑜𝑈 1 x x x 70.4 45.05 20.03 44.30 2 ✓ x x 71.32 47.77 22.5 45.44 3 x ✓ x 70.43 47.58 24.7 46.05 4 x x ✓ 71.34 45.11 20.65 44.85 5 ✓ x ✓ 70.97 47.93 23.47 45.85 6 x ✓ ✓ 71.53 47.98 24.73 46.58 7 ✓ ✓ x 71.13 49.14 25.91 47.06 8 ✓ ✓ ✓ 71.72 50.13 26.72 47.35 of our method, as demonstrated by the consistent improvements in higher IoU thresholds. Effectiveness of visual similarity. We estimate the distribution parameters by considering both visual similarity and temporal distance, as shown in Eq.(7). To evaluate the effectiveness of the visual similarity, we set 𝜆1 = 0 and remove it from the calculation of similarity between frames. The results are shown in Table 4, where “v-s" represents the visual similarity. Comparing the results in line 1 and 2 where the frame similarity computed by Eq.(7) is directly used as the probability, and comparing the results in line 6 and 8 where the Beta distribution is used for probability calculation, we observe improvements of more than 2.5% and 2% on 𝑅@1; 𝐼𝑜𝑈≥0.5, respectively, which clearly demonstrates the importance of the visual similarity. Effectiveness of intra-video loss. The intra-video loss, defined in Eq.(17), enforces similarities between language queries and pos- itive action candidates more than negative action candidates. It is computed in a single language-video pair and helps to rank all the action candidates, thus improving the accuracy of boundary estimation. To evaluate the effectiveness of the intra-video loss, we remove it for comparison, and the results are shown in Table 4. We observe that compared with the result in line 1 without the intra- video loss, the result in line 3 with the intra-video loss achieves significant improvements of more than 4% on 𝑅@1; 𝐼𝑜𝑈≥0.7, and about 2.5% on 𝑅@1; 𝐼𝑜𝑈≥0.5. This demonstrates that the intra- video loss effectively improves the localization accuracy, especially in high-precision scenarios. Similar trends of comparing the results in line 5 and line 8, line 2 and line 7, and line 4 and line 6, further verify the effectiveness of the intra-video loss. Effectiveness of inter-video loss. The inter-video loss, defined in Eq.(18), helps to leverage inter-sample information to learn the diversities of actions and facilitate model training in the early stage. However, it mainly focuses on learning the differences between dif- ferent action instances rather than the fine-grained details of action boundaries. To evaluate the effectiveness of the inter-video loss, we remove it for comparison, and the results are shown in Table 4. We observe that using the inter-video loss (lines 4, 5 and 8) improves the accuracy by about 1% compared with the results without the inter-video loss (lines 1, 2 and 7). However, this improvement is relatively small compared to that of the intra-video loss, suggesting that the intra-video loss is more effective in improving accuracy and localization performance, especially in terms of high precision, while the inter-video loss plays a complementary role in improving the diversity of learned action representations. 4.5 Comparison with State-of-the-art Methods We compare the proposed method with several state-of-the-art methods at different levels of supervision, including fully-supervised methods (CTRL [6], 2D-TAN [48], VSLNet [47]), weakly-supervised methods (TGA [25], SCN [13], LoGAN [33], CRM [9]), and frame- supervised methods (ViGA [4], LAS [42]). The comparison results on the TACoS and Charades-STA datasets are shown in Table 5 and Table 6, respectively. From the results, we have observations as follows: (1) Compared with the frame- supervised methods, i.e., ViGA [4], and LAS [42], our method achieves more than 10% improvements on the 𝑅@1; 𝐼𝑜𝑈≥0.3 and 𝑅@1; 𝐼𝑜𝑈≥0.5 on the challenging TACoS dataset, and more than 5% improvements on the 𝑅@1; 𝐼𝑜𝑈≥0.5 and 𝑅@1; 𝐼𝑜𝑈≥0.7 on the Charades-STA dataset, which demonstrates the effective- ness of the proposed distribution-based method on modeling the probabilities of pseudo-labels, especially in more difficult scenar- ios; (2) Compared with the fully-supervised methods shown in the upper parts of Table 5 and Table 6, our method achieves compara- ble results, demonstrating the huge potential of the performance of frame-supervised language-driven action localization; (3) Com- pared with the weakly supervised methods shown in the middle part of Table 6, our method outperforms all other methods in terms of all metrics by a large margin, showing the superiority of our method in the scenario that lacks full annotations. These results suggest that our method achieves satisfying performance on the TACoS and Charades-STA datasets and is a promising direction for language-driven action localization. 4.6 Qualitative Analysis We show several examples of action localization results of our method and the baseline model on the Charades-STA dataset in Figure 5. From the first three examples in Figure 5 (a), (b), and (c), we observe that the action boundaries predicted by our method are more accurate than the baseline model since the boundary frames participate in training with appropriate probabilities in our method. However, as shown in Figure 5 (d), both our method and the baseline model fail to locate the action boundaries because the video frames are too similar to distinguish, showing a lack of 5170 MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. Shuo Yang, Zirui Shang, and Xinxiao Wu Language query: a person kneeling on the floor talks on a phone Ours(Beta) Ground Truth Baseline 0s 8.38s 0s 8.44s 1s 10.43s 3.30s Beta Distribution 𝑃 ୠ ௙ 𝑇 (a) 0 Language query: person throw it on the floor Ours(Beta) Ground Truth Baseline 4.45s 9.85s 4.95s 8.94s 7.99s 𝑃 ୠ ௙ 𝑇 Beta Distribution 4.39s 9.80s 0 (b) (c) 0 Language query: a person puts food into a sandwich maker Ours(Beta) Ground Truth Baseline 0s 9.09s 0s 9.20s 1s 10.18s 1.48s Beta Distribution 𝑃 ୠ ௙ 𝑇 Language query: lastly the person takes a drink from a cup Ours(Beta) Ground Truth Baseline 12.23s 20.54s 1.96s 10.27s 14.65s 𝑃 ୠ ௙ 𝑇 Beta Distribution 9.80s 15.70s (d) 0 Figure 5: Examples of action localization results. “Baseline” denotes the results from the baseline model; “Ours (Beta)” denotes the results predicted by the model using the Beta distribution; “Beta Distribution” denotes the curve generated by Eq.(14). Table 5: Comparison with the state-of-the-art methods on the TACoS dataset. Upper part: Fully-supervised methods; Lower part: Frame-supervised methods. Methods 𝑅@1; 𝐼𝑜𝑈≥𝜇 𝑚𝐼𝑜𝑈 0.3 0.5 0.7 CTRL [6] 18.32 13.3 - - TripNet [8] 23.95 19.17 - - ABLR [45] 19.50 9.40 - - DEBUG [21] 23.45 11.72 - 16.03 VSLNet [47] 29.61 24.27 20.03 24.11 2D-TAN [48] 37.29 25.32 - - ViGA [4] 19.62 8.85 3.22 15.47 LAS [42] 23.64 10.00 3.35 17.39 Ours (Beta) 36.14 20.17 7.30 23.09 sufficient fine-grained motion understanding. It is worth noting that in all four examples in Figure 5, the estimated Beta distributions are reasonable and provide guidance information according to the annotated frame, thereby facilitating the cross-model alignment and boundary estimation during training. 5 CONCLUSION We have presented a novel probability distribution based method for frame-supervised language-driven action localization. By using distribution functions to model the probabilities of the action frame, as well as the starting and ending boundaries of the target action, our method is able to provide more accurate guidance in learning cross-modal alignment and boundary estimation to compensate Table 6: Comparison with the state-of-the-art methods on the Charades-STA dataset. Upper part: Fully-supervised meth- ods; Middle part: Weakly-supervised methods; Lower part: Frame-supervised methods. Methods 𝑅@1; 𝐼𝑜𝑈≥𝜇 𝑚𝐼𝑜𝑈 0.3 0.5 0.7 CTRL [6] - 23.63 8.89 - 2D-TAN [48] - 39.70 23.31 - LGI [26] 72.96 59.46 35.48 51.38 VSLNet [47] 70.46 54.19 35.22 50.02 TGA [25] 32.14 19.94 8.84 - SCN [13] 42.96 23.58 9.97 - LoGAN [33] 51.67 34.68 14.54 - CRM [9] 53.66 34.76 16.37 - LAS [42] 60.40 39.22 20.17 39.77 ViGA [4] 71.21 45.05 20.27 44.57 Ours (Beta) 71.72 50.13 26.72 47.35 for the lack of supervision, thus successfully improving the accu- racy of action localization. Extensive experimental results on two benchmark datasets demonstrate the effectiveness of our method. We believe that the distribution-based framework will be a promis- ing direction for further research in the field of frame-supervised language-driven action localization. ACKNOWLEDGMENTS This work was supported in part by the Natural Science Foundation of China (NSFC) under Grant No 62072041. 5171 Distribution Based Frame-supervised Language-driven Action Localization MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada. REFERENCES [1] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. 2017. Localizing moments in video with natural language. In Proceedings of the IEEE international conference on computer vision. 5803–5812. [2] Amy Bearman, Olga Russakovsky, Vittorio Ferrari, and Li Fei-Fei. 2016. What’s the point: Semantic segmentation with point supervision. 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In Proceedings of the 28th ACM International Conference on Multimedia. 4098–4106. 5173 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 Explicit Granularity and Implicit Scale Correspondence Learning for Point-Supervised Video Moment Localization Anonymous Authors ABSTRACT Video moment localization (VML) aims to identify the temporal boundary of the target moment semantically matching the given query. Existing approaches fall into three paradigms: fully-supervised, weakly-supervised, and point-supervised. Compared to other two paradigms, point-supervised VML strikes a balance between lo- calization accuracy and annotation cost. However, it is still in its infancy due to the following two challenges: explicit granularity alignment and implicit scale perception, especially when facing complex cross-modal correspondences. To this end, we propose a Semantic Granularity and Scale Correspondence Integration (SG- SCI) framework aimed at modeling the semantic alignment between video and text, leveraging limited single-frame annotation infor- mation for correspondence learning. It explicitly models semantic relations of different feature granularities and adaptively mines the implicit semantic scale, thereby enhancing and utilizing modal feature representations of varying granularities and scales. SG-SCI employs a granularity correspondence alignment module to align semantic information by leveraging latent prior knowledge. Then we develop a scale correspondence learning strategy to identify and address semantic scale differences. Extensive comparison experi- ments, ablation studies, and necessary hyperparameter analyses on benchmark datasets have demonstrated the promising performance of our model over several state-of-the-art competitors. CCS CONCEPTS • Information systems →Novelty in information retrieval; Multimedia and multimodal retrieval. KEYWORDS Cross-modal Moment Localization; Cross-Modal Retrieval; Corre- spondence Learning ACM Reference Format: Anonymous Authors. 2024. Explicit Granularity and Implicit Scale Cor- respondence Learning for Point-Supervised Video Moment Localization. In Proceedings of the 32nd ACM International Conference on Multimedia (MM’24), October 28-November 1, 2024, Melbourne, Australia. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn Unpublished working draft. Not for distribution. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. ACM MM, 2024, Melbourne, Australia © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM https://doi.org/10.1145/nnnnnnn.nnnnnnn Query : A man poses for a picture in front of them … … Video Fully-supervised Annotation:[98.31,103.34] Point-supervised Annotation:[101.16] Weakly-supervised Annotation:[-] 98.31 103.34 101.16 (a) Video Moment Localization (b) Explicit Granularity Alignment and Implicit Scale Perception … … GT Fine-grained Irrelevant Query Coarse-grained Mismatch … … Query1 Query2 Semantic Inclusion GT2 SF PM GT1 The man adds pepper to the egg The man is preparing to cook A woman is playing music, and a little boy walks by Video Figure 1: (a) Illustrative examples of Video Moment Localiza- tion (VML). (b) Illustration of explicit granularity alignment and implicit scale perception among VML. GT: Ground Truth; PM: Prediction Moment; SF: Supervised Frame. (Left) Fine- grained word-frame irrelevance in green and coarse-grained sentence-moment mismatch in blue. (Right) Illustration of the semantic inclusion relationships between textual and visual correspondences. 1 INTRODUCTION Video moment localization (VML), which refers to localizing a visual moment corresponding to a given textual query, is a funda- mental task in video understanding. It benefits many important application scenarios, such as multimedia retrieval [45, 48] and smart human–computer interaction [37, 43]. Current efforts [14, 44, 50, 53] focus on addressing both fully and weakly supervised VML paradigms. As shown in Figure 1(a), the fully-supervised method requires the ground-truth moment annotations for training, which is laborious and time-consuming to obtain [29]. The weakly-supervised method is more flexible because it does not require moment annotations, but results in a significant drop in performance [10]. To balance accuracy with annotation cost, the point-supervised paradigm is proposed [21, 25]. Point- supervised methods require a single frame annotation from the ground truth, which is more practical and flexible compared to fully supervised annotation, costing only 1/6 as much [24]. Prior research [2, 4, 18, 35], to enhance video-language compre- hension, can be classified into two primary approaches: granularity modeling and scale modeling. Granularity modeling involves delin- eating relationships among various features (such as frame-word and moment-sentence) through the explicit boundaries present in video-language, facilitating the differentiation of data across vary- ing entity granularities. In contrast, scale modeling derives from the intricate semantic logics and inherent hierarchical structures within textual and visual domains. This approach acknowledges 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 ACM MM, 2024, Melbourne, Australia Anonymous Authors 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 implicit semantic scale in video and language interaction that exists within an alternative modal space. To be specific, as shown in Figure 1(b), existing point-supervised labeling inevitably introduces two accompanied challenges: 1) Ex- plicit Granularity Alignment. According to [10, 12], on the one hand, single-frame annotations are randomized within intervals, causing clear mismatches at a fine-grained level (frame-word); on the other hand, the incomplete information on interval annotations hinders the direct use of correspondences at a coarse-grained level (moment-query). Undoubtedly, this challenge poses a significant obstacle to effective language and visual alignment. 2) Implicit Scale Perception. Prior studies [9, 16] note that query sentences for the same video may vary in semantic scale. This issue is espe- cially noticeable in the point-supervised approach. These methods struggle to capture temporal relationships within a video because of using only single-frame annotations, leading to a lack of effective contextual modeling constraints. As a result, the essential semantic scale information remains unmodeled, impeding temporal learning and video comprehension. To navigate these challenges, we introduce a novel Semantic Granularity and Scale Correspondence Integration (SG-SCI) frame- work that integrates a Granularity Correspondence Alignment (GCA) module and a Scale Correspondence Learning (SCL) strategy. Firstly, the GCA module is engineered to enhance the interaction of video- language. By employing a fine-grained alignment approach, it es- tablishes a more detailed and comprehensive mapping between video frames and textual descriptions. This module not only facili- tates a deeper understanding of the video content but also ensures that even the less prominent frames find relevance in the corre- sponding textual narrative. Secondly, the SCL strategy addresses the disparity in temporal scale. It is designed to learn latent seman- tics adaptively across varying temporal scales, enabling the model to assimilate and correlate extensive video sequences with succinct textual queries. This strategy ensures a more robust and contextu- ally aware matching process, thereby enhancing the accuracy and adaptability. Consequently, this framework offers a more nuanced and adaptable solution for point-supervised VML. To the best of our knowledge, it is the first work on integrating explicit granularity alignment and implicit scale perception into point-supervised VML. Our approach effectively mitigates the gran- ularity and scale-related challenges by the synergy of the innovative component and strategy. It boosts the precision of aligning video moments to text queries and enhances the model’s robustness. In summary, the main contributions are as follows: • Model Contribution. We introduce an innovative Granular Correspondence Alignment module. Specifically, it is designed to improve the explicit correspondence relation across varying granularities among different modalities. • Strategy Contribution. Under the framework of point-supervised, we develop a Scale Correspondence Learning strategy, which is pivotal in capturing the implicit semantic scale in correspondence learning. • Experimental Contribution. Extensive experiments on two benchmarks, i.e., Charades-STA [12] and TACoS [33], validate the effectiveness and superiority of our model. The codes and settings are released at https://anonymous.4open.science/r/SG-SCI. 2 RELATED WORK 2.1 Video-Language Modeling Current research can be divided into two main categories depending on the use of a modeling approach: feature granularity modeling and semantic scale modeling. In terms of entities, the former is explicit while the latter is implicit. Early explicit modeling approaches [1, 12] mainly used global sentence-level alignment to enhance the semantics of visual fea- tures, but this approach overlooked fine-grained semantics such as words. As a result, several studies [4, 35, 49] have investigated various word-frame interactions using attention mechanisms to capture the relationships between visual cues and textual queries. Recent research [38, 47, 52] has recognized the significance of local phrase patterns or tokens in sentences for video moment retrieval. Compared to explicit modeling, implicit scale modeling [2, 11, 18, 19, 39] utilizes multi-scale relations to achieve better grounding results. These relations mainly exist at the video-level and language- level. However, all grounding methods overlook the possibility of different semantic scales within the same level (e.g., sentence level). Accordingly, DualMIL [9] extends multiple instance learning into a two-level framework. Although the aforementioned modeling strategies are effective, most methods do not fully utilize their potential when dealing with limited supervisory information. Therefore, our focus is on extracting both implicit and explicit information to accurately locate boundaries in point-supervised VML. 2.2 Video-Language Correspondence Learning In the domain of video-language learning, the misalignment be- tween textual descriptions and corresponding visual content intro- duces significant challenges, necessitating the adoption of corre- spondence learning as an innovative approach [15, 17, 20]. This paradigm shift is epitomized by the introduction of MIL-NCE [31], which pioneers the strategy of aligning video clips with adjacent sentences to diminish the effects of misalignment. In contrast, Tang et al. [40] improves video description quality by integrat- ing an external image captioning model, emphasizing data-driven enhancement over immediate error correction. Our method differs itself from preceding endeavors through two principal innovations. Firstly, we go beyond the traditional focus on either model architectures or supervisory strategies by enhancing improvements through the synergistic interaction be- tween model and strategy learning. Secondly, we expand the use of correspondence learning beyond segmented multi-modal data. This pioneering adaptation allows for precise moment localization with point-supervised for the first time. 2.3 Video Moment Localization Different from existing fully and weakly-supervised VML stud- ies [12, 14, 27, 30, 44, 50, 53], point-supervised VML strikes a deli- cate balance between annotation efforts and model performance by leveraging a single frame from the localization moment [10, 21, 25]. Compared to fully-supervised VML, point-supervised significantly reduces the cost of annotation data and it provides more comprehen- sive information than weakly supervised learning. By eliminating 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 Explicit Granularity and Implicit Scale Correspondence Learning for Point-Supervised Video Moment Localization ACM MM, 2024, Melbourne, Australia 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 A little boy holding a yellow ball walks by. (a) Feature Extraction Video Encoder Text Encoder (b) Multi-modal Interaction Granularity Correspondence Alignment (c) Moment Localization (d) Granularity Correspondence Alignment (e) Scale Correspondence Learning Training Inferencing Moment to Frame Frame to Moment 1 2 3 4 T … Positional Embedding Self-Attention Block Self-Attention Block boy yellow ball walks Cross-Attention Block … Step 1 Step 2 … Pooling Temporal Weight Semantic Score Gaussian Distribution … p ∼N ! µf, σ2 f " Granularity Perception Matrix yellow boy walks ball 𝑠!! 𝑠!" 𝑠#! 𝑠#" 𝑠 $! 𝑠$ " 𝑠!% 𝑠#% 𝑠

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" 𝑚% # 𝑚%#$ # KL Divergence element dot product matrix multiplication element addition element average … … … 1 2 3 4 T … 𝒂𝒗𝒈 𝒂𝒗𝒈 𝑭 ⋅ ⋅ ⋅ … 3 𝑇−1 ⋅ Figure 2: Schematic illustration of the proposed SG-SCI model. We first perform (a) Feature Extraction on both video and language separately, followed by (b) Multi-modal Interaction, which combines the obtained cross-attention features and similarity information, feeding them into the (d) Granularity Correspondence Alignment module. After the interaction, the features are input into the (c) Moment Localization module. During the training phase, we apply a (e) Scale Correspondence Learning strategy to compare semantics at diverse scales. the need for precise start and end timestamps for the target moment, a quick "glance" at the video and the selection of a single frame are often sufficient, making point-supervised methods applicable to sce- narios with incomplete annotation information [46]. The concept of point-supervised was initially introduced by Bearman et al. [3] in the context of semantic segmentation tasks and Cui et al. [10] applied the concept of point-supervised to VML and introduced the ViGA model, which aligns Gaussian distributions generated from supervised frame positions with cross-modal attention. Subsequent works have built upon ViGA’s foundation, D3G [25] is proposed to align features of sentence-moment pairs and dynamically miti- gate annotation bias using Gaussian distributions, while CFMR [21] develops a concept-driven multi-modal alignment mechanism to circumvent the need for cross-modal interaction modules during the inference process. Despite efforts to enhance retrieval performance, existing work still falls short in effectively modeling the semantic granularity relationship between two modalities. Furthermore, due to the con- straints of the supervised information, it struggles to capture the overall action changes within a moment. 3 METHODOLOGY This section commences by defining the problem and outlining the core pipeline of point-supervised video moment localization. After this, we will explore the insights behind our innovative Granularity Correspondence Alignment method, highlighting its main elements. Finally, we conclude with an exposition of our Scale Correspondence Learning strategy, showing its significance in our research. Figure 2 presents an overview of our proposed SG-SCI. 3.1 Problem Definition In this work, we aim to address the challenge of cross-modal video localization under point-supervised. The dataset comprises a series of quadruples and is denoted as D, which is expressed as follows, D = {(V 𝑖, Q𝑖,𝜏𝑠 𝑖,𝜏𝑒 𝑖) | 𝑖= 1 to 𝑁}, (1) where V 𝑖represents an untrimmed video, Q𝑖denotes the associ- ated query sentence, and 𝜏𝑠 𝑖, 𝜏𝑒 𝑖are the start and end timestamps respectively. The target video moment aligns with the query Q𝑖. Contrasting with a fully-supervised paradigm, our training is based on a collection of triplet annotations, symbolized as A = {(V 𝑖, Q𝑖,𝜏𝑚 𝑖) | 𝑖= 1 to 𝑁}, where 𝜏𝑚 𝑖 signifies a randomly selected point within the interval defined by 𝜏𝑠 𝑖and 𝜏𝑒 𝑖. During the infer- ence phase, the objective is to precisely localize the relevant video moment for each query within the dataset D. 3.2 Pipeline of Point-supervised VML To fulfil the task of Point-supervised VML, we first introduce the pipeline which can be categorized into three parts: Featurn Extrac- tion, Multi-modal Interaction, and Moment Localization. 3.2.1 Feature Extraction. Before multi-modal interaction, we ex- tract the visual and textual features, and then incorporate learnable positional embedding into the extracted features. Visual Representation. Given an untrimmed video V, our approach utilizes a pre-trained CNN, i.e., C3D [41] or I3D [6], to extract visual features to ensure fair comparison. The extracted features are then processed through a fully-connected (FC) layer, obtaining a visual representation denoted as 𝑉= {𝑣1, 𝑣2, . . . , 𝑣𝑇}, 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 ACM MM, 2024, Melbourne, Australia Anonymous Authors 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 where 𝑇represents the number of frames sampled at intervals in the video and 𝑣𝑖denotes the 𝑖-th frame. To perceive the time of the video, we incorporate learnable Po- sitional Embedding (PE) into the model and get enhanced visual representation ¯ 𝑉∈R𝑇×𝑑, where 𝑑represents the dimension of the visual representation. Text Representation. For a given query Q, we employ the pre- trained Glove model [32] to extract textual features. A bi-directional gated recurrent unit (Bi-GRU) is employed to obtain the latent se- quential order of original sentence, and get new textual representa- tion 𝑄= {𝑞1,𝑞2, . . . ,𝑞𝐿}, where 𝐿indicating the number of words in the query. Similar to the visual representation, PE is also added to the text representation, obtaining the enhanced features ¯ 𝑄∈R𝐿×𝑑. 3.2.2 Multi-modal Interaction. To capture the intra-model seman- tics, we utilize ¯ 𝑉and ¯ 𝑄through the self-attention layer [42] to generate the intra-modal representation ˆ 𝑉and ˆ 𝑄. Similarly, we use the cross-modal multi-head attention to obtain the cross-modal representation ˜ 𝑉= 𝐴𝑡𝑡𝑛( ˆ 𝑄, ˆ 𝑉) and ˜ 𝑄= 𝐴𝑡𝑡𝑛( ˆ 𝑉, ˆ 𝑄), where 𝐴𝑡𝑡𝑛(·) is formulated as follows, 𝐴𝑡𝑡𝑛( ˆ 𝑄, ˆ 𝑉) = softmax( ˆ 𝑄ˆ 𝑉𝑇 √ 𝑑 ) ˆ 𝑉, 𝐴𝑡𝑡𝑛( ˆ 𝑉, ˆ 𝑄) = softmax( ˆ 𝑉ˆ 𝑄𝑇 √ 𝑑 ) ˆ 𝑄. (2) Subsequently, to serve the subsequent modules, we take out the last layer of cross-modal attention scores as the cross-modal attention scores 𝐴𝑞→𝑣and 𝐴𝑣→𝑞, which will be further illustrated in 3.3. We input the features and attention scores into our proposed granularity correspondence alignment module to obtain the multi- granularity representation ˜ 𝑉𝑓and ˜ 𝑄𝑤: ˜ 𝑉𝑓= GCA( ˜ 𝑉, ˜ 𝑄,𝐴𝑞→𝑣), ˜ 𝑄𝑤= GCA( ˜ 𝑄, ˜ 𝑉,𝐴𝑣→𝑞), (3) where GCA(·) is the granularity correspondence alignment mod- ule. So far, we have obtained the multi-granularity representa- tion ˜ 𝑉𝑓and ˜ 𝑄𝑤via multi-modal interaction. The representation is compound of multi-level granularities information, assisting cross- modal semantic alignment. In order to integrate the semantic in- formation of the whole sentence, we use the max-pooling function F(·) to obtain the sentence representation ˜ 𝑄𝑠as follows, ˜ 𝑄𝑠= F( ˜ 𝑄𝑤). (4) 3.2.3 Moment Localization. Moment localization aims to locate the start and end timestamps based on the obtained representation. In the training stage, unlike fully-supervised scenario, a random point within the target video moment is accessed while it is absent during inference. Because of the divergence between the two stages, we will elaborate on both of them subsequently. Training Stage. In the training process, we use sliding windows to slice ˜ 𝑉𝑓, and generate candidate moments. Gaussian distribution is utilized to measure the moments’ temporal features and cosine similarity of semantic features. The temporal information is defined as follows, 𝐺𝑖= 1 √ 2𝜋𝜎 exp © « − (𝑖−𝜏𝑚) · 2 𝐿𝑣−1 2 2𝜎2 ª ® ® ¬ , (5) where 𝑖is the 𝑖-th frame, 𝜏𝑚is the index of the supervised frame, 𝜎 is a hyperparameter, and 𝐿𝑣is the length of the video. The semantic information 𝑆𝑖is the cosine similarity between ˜ 𝑉𝑓(𝑖) and ˜ 𝑄𝑠. These temporal and semantic analyses are integrated and applied in our scale correspondence learning strategy, detailed in Sec. 3.4. Inference Stage. The inference involves two primary steps, consisting of identifying the key point that best matches query and expand from this point to get the predicted video moment most similar to the query. 3.3 Granularity Correspondence Alignment In this section, we introduce a novel granularity correspondence alignment module within the framework of point-supervised VML, which is designed to adaptively regulate the granularity relation- ship between different modalities. By exploring semantic relation- ships across modalities, the module enriches the original modal representation and supplements existing semantic alignment with information across various granularities, ensuring symmetry in intermodal interactions. Unlike existing methods that unconsciously interact the small- est granularity units (frames and words) in representations across modalities, our main insights derive from exploiting the attention- perceived matrix and utilizing it to capture potential cross-modal semantic granularity information. Specifically, we use the matrix consisting of cross-modal attention scores as a granularity percep- tion matrix as follows, 𝐴𝑞→𝑣=           𝑠11 𝑠12 · · · 𝑠1𝐿 · · · · · · · · · · · · 𝑠𝑖1 𝑠𝑖2 · · · 𝑠𝑖𝐿 · · · · · · · · · · · · 𝑠𝑇1 𝑠𝑇2 · · · 𝑠𝑇𝐿           , (6) where 𝐴𝑞→𝑣∈R𝑇×𝐿is the cross-modal attention score matrix, 𝑇 and 𝐿are the number of frames and words, respectively, and 𝑠𝑖𝑗is the attention score between the 𝑖-th frame and the 𝑗-th word. The differences of scores in each column represent how much attention the word pays to the video frame. Therefore, we consider using max function to aggregate the information in each row to obtain the potential prior distribution ˜ 𝑉𝑝∈R𝑇of the complete query with respect to the video frames, formulated as, ˜ 𝑉𝑝= max(𝐴𝑞→𝑣) = [max(𝑠1·), max(𝑠2·), . . . , max(𝑠𝑇·)]𝑇. (7) Next, we calculate the cosine similarity between the query and the original video frames to obtain the prior distribution of the complete query with respect to the video frames, ˜ 𝑉𝑔= 𝑐𝑜𝑠( ˜ 𝑉, ˜ 𝑄𝑠), (8) where 𝑐𝑜𝑠(·) is the cosine similarity function. Subsequently, we use the finest granularity information from attentional perception and pooled global granularity information, which are averaged and used as adaptive visual granularity perceptual features ˜ 𝑉𝑐= 1 2 ( ˜ 𝑉𝑔+ ˜ 𝑉𝑝), In this way, we can obtain the visual granularity perception vector 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 Explicit Granularity and Implicit Scale Correspondence Learning for Point-Supervised Video Moment Localization ACM MM, 2024, Melbourne, Australia 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 ˜ 𝑉𝑐, which can be used to enhance the original visual representation. On this basis, we use the obtained features to remodel the cross- modal fusion with text feature, ˜ 𝑉𝑎= ˜ 𝑉𝑐⊙˜ 𝑉𝑓. (9) After reaggregation, we integrate self-attention-based fine-grained features with multi-scale visual perception features. This enhances fine-grained entity alignment and focuses on overall perception, thereby improving representation. We then apply these features for cross-modal semantic interactions, using prior semantic knowledge to refine visual-word correlations. Finally, we use the weighted mean approach in order to control the original modal feature information, thus preventing noise inter- ference due to excessive introduction of cross-modal information as follows, ˜ 𝑉𝑓= 𝛼˜ 𝑉+ (1 −𝛼) ˜ 𝑉𝑎, (10) where 𝛼is the mean weighted factor. We use the same strategy applied to another modality. Therefore, we can obtain the final cross- modal representation ˜ 𝑉𝑓and ˜ 𝑄𝑤via the multi-modal interaction. 3.4 Scale Correspondence Learning To improve the model’s understanding ability under different scales of moments, we utilize potential frame-moment correspondence semantic information, as the single-frame supervised information alone is not sufficient for effective moment semantics. To be specific, the target of optimization consists of three parts, the first part models the semantic and temporal information of the fused features from a global perspective. The second part uses point annotation to compare the differences between potential positive and negative samples in different intervals to effectively capture the information about the change of actions within the intervals. The third part aims to exploit the single point information in the global moment that is most similar to the query, which in turn enriches the priori knowledge and enhances the guidance of cross-modal semantics. 3.4.1 Global Alignment Loss. We take the cross-entropy loss to force the representation information we get to be close to the in- formation provided by the supervised frames in terms of temporal distance and semantic distance, 𝐿𝑔= − 𝑇 ∑︁ 𝑖=1 𝐺𝑖log𝑆𝑖, (11) where 𝐺𝑖is the Gaussian distribution weight of the 𝑖-th frame, and 𝑆𝑖is the semantic similarity score of the 𝑖-th frame. 3.4.2 Frame-moment Correspondence Loss. Due to lacking of bound- ary annotations, a single point’s information inadequately repre- sents the interval matching the query. This makes us to consider using video moments that encompass point annotation as potential positives. We construct binary labels to facilitate interval percep- tion: moments containing the point annotation are labeled 1, while others are labeled 0, as follows, y𝑖= ( 1, 𝜏𝑚∈M𝑖, 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒, (12) where M𝑖is the 𝑖-th moment by sliding window. Due to the latent differences in temporal information of these moments, we introduce Gaussian distribution weights to further construct the soft labels ˜ y𝑖, which can be formulated as: ˜ y𝑖= y𝑖W(M𝑖), (13) where W(·) = 𝐺(𝑠) ∗𝐺(𝑒), 𝐺(𝑠) and 𝐺(𝑒) are the Gaussian distri- bution weights at the beginning and end of M𝑖, respectively. Since it is inevitable that there will be multiple positive sample moments belonging to a query, it is inappropriate to view similarity score learning as a 1-in-N classification problem with cross-entropy loss. To this end, we utilize the Kullback-Leibler divergence to construct frame-moment correspondence loss 𝐿𝑓→𝑚 𝑐 , as follows, 𝐿𝑓→𝑚 𝑐 = 𝑁 ∑︁ 𝑖=1 ˜ y𝑖log ˜ y𝑖 S𝑖 , (14) where S𝑖is the cosine similarity calculated by moment M𝑖and query feature ˜ 𝑄𝑠. 3.4.3 Moment-frame Correspondence Loss. In the beginning of lo- cation process, we focus on identifying a point that align most closely with the query, enabling our model to discern and empha- size subtle points’ differences. We separate essential information from positive sample moments, concentrating on feature variances. Specifically, we extract detailed point data reflecting semantic labels across moments, leveraging this to enrich point annotation with inherent disparities. This process can be represented as follows, K𝑖= ˜ 𝑉𝑓(𝜃),𝜃= arg max 𝑗∈[𝑠,𝑒] 𝑆𝑗, (15) where 𝑠and 𝑒are the start and end index of the moment M𝑖. For label construction, we use both hard sample labels and soft labels with Gaussian weights, just as the Eqn. (12) and (13). We then employ KL divergence to analyze differences between positive and negative samples, using these insights to refine our alignment process, as follows, 𝐿𝑚→𝑓 𝑐 = 𝑁 ∑︁ 𝑖=1 ˜ y𝑖log ˜ y𝑖 K𝑖 . (16) By combining the global loss 𝐿𝑔and correspondence loss 𝐿𝑐, we obtain the final loss for model optimization, as follows, 𝐿= 𝐿𝑔+ 𝛽𝐿𝑓→𝑚 𝑐 + 𝛾𝐿𝑚→𝑓 𝑐 , (17) where 𝛽and 𝛾are used to balance the focus between moments and frames, avoiding the model over underscore the sample information. 4 EXPERIMENTS This section will begin by presenting the experimental settings. Subsequently, we will conduct comparative experiments, compre- hensive ablation studies, and evaluate the effectiveness of SG-SCI to answer the following three research questions (RQs): • RQ1: Is our proposed SG-SCI able to outperform several state- of-the-art competitors on VML? • RQ2: Is each component of our SG-SCI helpful for boosting the localization performance? • RQ3: How do hyperparameters affect model capability? 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 ACM MM, 2024, Melbourne, Australia Anonymous Authors 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 4.1 Datasets The experiments that follow are conducted on two benchmark datasets: Charades-STA [12] and TACoS [33]. Charades-STA [12]: It is constructed on the Charades dataset [36] and contains 16,128 "moment-query" pairs with an average video duration of 30 seconds. Following the standard split strategy [12], we divided the dataset into 12,408 and 3,720 "moment-query" pairs for training and testing, respectively. TACoS [33]: It is built upon MPII Cooking Compositive dataset [34] and only covers cooking activities that contain pairs of queries with very similar visual information. It consists of 127 untrimmed videos with the average duration of 320 seconds and 18,818 queries. In particular, we use point-supervised information instead of boundary supervision information in the original dataset setting, just like existing effort [10], where the point-supervised information comes from a random point in the boundary. 4.2 Experimental Settings 4.2.1 Evaluation Metrics. Following the existing work [10, 21, 25], we choose 𝑅𝑛@𝑚and mean averaged IoU (mIoU) as protocols to evaluate the performance of moment localization. To be specific, 𝑅𝑛@𝑚refers to the percentage of predictions for which the tempo- ral Intersection over Union (IoU) surpasses the thresholds 𝑚within the top-𝑛of the sorted results, and mean averaged IoU (mIoU) rep- resents the average IoU across all test samples. Since our method localizes the target moment with the highest coordinate probability, we set 𝑛= 1 and 𝑚∈{0.3, 0.5, 0.7}. The higher 𝑅𝑛@𝑚and mIoU are better. 4.2.2 Implementation Details. In our work, we employ the pre- trained I3D [6] and C3D [41] network to extract visual features from Charades-STA [12] and TACoS [33] respectively. Consistent with prior research [10], we keep the word embedding module fixed and utilize the 840B GloVe [32] to construct a comprehensive vocabulary. The model dimension 𝑑𝑚𝑜𝑑𝑒𝑙is set to 512, and we employ AdamW [28] with a learning rate of 1𝑒−4, which decays by half when reaching a plateau during training. By default, we set 𝛼= 0.3, 𝛽= 𝛾= 0.1, and 𝜎= 0.04 in both datasets. The batch sizes for the Charades-STA and TACoS are empirically set to 512 and 64, respectively. All experiments are conducted on a NVIDIA GeForce RTX 4090 with 24GB memory. 4.3 Performance Comparison (RQ1) In this subsection, we compare the proposed method with different kinds of state-of-the-art methods, including fully-supervised meth- ods (2D-TAN [51], SS [11], FVMR [13], ADPN [7], MS-DETR [22]), weakly-supervised methods (SCN [26], CNM [54], CWG [8], CPL [55], IRON [5], PPS [23]) and point-supervised methods (ViGA [10], PSVTG [46], CFMR [21], D3G [25]). To ensure the validity of the comparison, the majority of the selected methods are drawn from the last three years. The comparisons on Charades-STA and TACoS are encapsulated in Table 1 and Table 2 respectively. Based on the data presented, the following observations can be derived: • Our method mines more correspondence information com- pared with existing point-supervised methods. While tra- ditional point-supervised methods focus on the interaction of Table 1: Performance comparison on Charades-STA with different supervision methods. Bold means the best result in point-supervised method and underline means the second best. Type Method R1@0.3 R1@0.5 R1@0.7 mIoU Fully-supervised 2D-TAN [AAAI20] [51] - 50.62 28.71 - SS [ICCV21] [11] - 56.97 32.74 - FVMR [ICCV21] [13] - 55.01 33.74 - ADPN [ACMMM23] [7] 70.35 55.32 37.47 51.15 Weakly-supervised CWG [AAAI22] [8] 43.41 31.02 16.53 - CPL [CVPR22] [55] 66.40 49.24 22.39 - IRON [CVPR23] [5] 70.28 51.33 24.31 - PPS [AAAI24] [23] 69.06 51.49 26.16 - Point-supervised ViGA [SIGIR22] [10] 71.21 45.05 20.27 44.57 PSVTG [TMM22] [46] 60.40 39.22 20.17 39.77 CFMR [ACMMM23] [21] - 48.14 22.58 - D3G [CVPR23] [25] - 43.82 20.46 - SG-SCI (Ours) 70.30 52.07 27.23 46.77 Table 2: Performance comparison on TACoS with different supervision methods. Bold means the best result in point- supervised method and underline means the second best. Type Method R1@0.3 R1@0.5 R1@0.7 mIoU Fully-supervised 2D-TAN [AAAI20] [51] 37.29 25.32 - - SS [ICCV21] [11] 41.33 29.56 - - FVMR [ICCV21] [13] 41.48 29.12 - - MS-DETR [ACL23] [22] 47.66 37.36 25.81 35.09 Weakly-supervised SCN [AAAI20] [26] 11.72 4.75 - - CNM [AAAI22] [54] 7.20 2.20 - - CPL [CVPR22] [55] 11.42 4.12 - - Point-supervised ViGA [SIGIR22] [10] 19.62 8.85 3.22 15.47 PSVTG [TMM22] [46] 23.64 10.00 3.35 17.39 CFMR [ACMMM23] [21] 25.44 12.82 - - D3G [CVPR23] [25] 27.27 12.67 4.70 - SG-SCI (Ours) 37.47 20.59 8.27 23.83 sentence-moments [25], our method involves multi-granularity interactions among complex video, concise textual descriptions, and point annotation. Therefore, the model can consider the in- teraction between sentences and point annotation at multiple granularities during the training process, resulting in better cross- modal information interaction. As illustrated in Table 1, in the Charades-STA dataset, SG-SCI achieves 4.65% and 6.77% higher than CFMR [21] and D3G [25] in 𝑅1@0.7, respectively. • SG-SCI reduces the performance disparity between fully- supervised methods and point-supervised methods. Lack- ing of annotated timestamps, the positioning accuracy of point- supervised methods is lower compared to existing supervised learning methods. By learning features across different scales, SG- SCI exploits the information contained in the supervised frames, to some extent compensating for the performance gap caused by different supervised information. In the Charades-STA dataset, our method even outperforms 2D-TAN [51], fully-supervised method, by 1.45% in terms of 𝑅1@0.5. • Trivial annotation cost contributes to localization. The ex- istence of frame annotation provides the model with a clear direction during iterative convergence, and our proposed corre- spondence module enables the model to continuously converge during gradient backpropagation based on the multi-granularity information from query and frame annotation. As evidenced in Table 1 and Table 2, our approach consistently outperforms the cutting-edge weakly-supervised models. 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 Explicit Granularity and Implicit Scale Correspondence Learning for Point-Supervised Video Moment Localization ACM MM, 2024, Melbourne, Australia 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 (a) Component Ablation. (b) GCA Ablation. (c) Mean Weighted Factor. Figure 3: Impact of (a) GCA and SCL, (b) multiple granularities in GCA, and (c) the mean weighted factor 𝛼in GCA. Evaluation performed on Charades-STA. Table 3: Comparison of different loss ablation for our frame- work. "means retaining it and %means removing it. Global F-M M-F R1@0.3 R1@0.5 R1@0.7 mIoU " % % 66.83 37.74 14.14 40.81 % " % 50.56 27.96 10.27 31.04 % % " 41.13 23.87 9.46 26.33 % " " 47.50 25.59 9.70 29.13 " % " 70.00 44.70 19.81 44.04 " " % 69.89 42.07 17.50 43.28 " " " 70.30 52.07 27.23 46.77 • SG-SCI can handle more challenging task. In contrast to Charades-STA, TACoS contains longer videos with shorter re- trieved moments, and each frame within the video exhibits a high degree of semantic approximation, demanding a heightened abil- ity from the model to perceive boundaries and differentiate simi- lar moments. Consequently, existing weakly-supervised methods may struggle to address this scenario, resulting in a significant absence of the 𝑅1@0.7 metric in Table 2. However, compared to existing point-supervised methods, like ViGA [10] and D3G [25], our approach exhibits great performance in 𝑅1@0.7. 4.4 In-depth Analysis (RQ2 & RQ3) To demonstrate each component of SG-SCI, we conducted extensive ablation experiments on Charades-STA. 4.4.1 Ablation Study. • Impact of GCA and SCL. To discuss whether the proposed GCA and SCL can achieve better performance, we simplify the pipeline of SG-SCI and set it as the baseline. Specifically, after multi-modal interaction, we train the model by calculating the KL divergence of the similarity distribution between the query and each frame with a Gaussian distribution. To demonstrate the effectiveness of SCL, we conduct experiments by setting the soft labels to 1. From the Figure 3(a), it can be seen that both GCA and SCL effectively enhance the localization ability of the baseline, especially in 𝑅1@0.7. • Effect of multiple granularities in GCA. As described in Sec 3.3, GCA not only helps to understand the video contents more deeply but also ensures that less significant frames have relevance in the corresponding textual narrative. We validate the effectiveness of multi-granularity information interaction on cross-modal representation, with specific results shown in Figure 3(b). It can be seen that the multi-granularity interaction proposed in GCA has effectively improved the localization effect by enhancing single-modal representation. At the same time, increasing the multi-granularity interaction of queries can en- hance the model’s ability to perceive fine-grained boundaries, i.e., improve the 𝑅1@0.7 by 6.10%. • Effectiveness of different loss. The final loss contains three parts: global loss, frame-moment correspondence loss and moment- frame correspondence loss. To evaluate the effectiveness of them, we remove some of them for comparison and the results are shown in Table 3, and we find that scale correspondence learning can inspire the potential of the model for boundary perception. In the presence of global loss, the frame-moment correspondence loss and moment-frame correspondence loss alone can enhance performance. The combination of the two can produce compre- hensive performance improvement, and the 𝑅1@0.7 even gets more than 10% improvement than only using global loss, showing that scale correspondence learning can enhance the ability to capture key information and boundary perception. 4.4.2 Parameter sensitivity. • The mean weighted factor 𝛼in GCA. An important hyperpa- rameter in GCA is 𝛼, which is used when combining the different granularities. It represents how much information is interacted and 𝛼= 1 represents merely using global feature information while 𝛼= 0 denotes only utilizing the fine granularity infor- mation. The specific results are shown in Figure 3(c). It can be observed that each metric shows a trend of first rising and then falling. Moreover, different difficulty of tasks represent diverse 𝛼: for coarse-grained localization task (𝑚= 0.3), 𝛼= 0.5 is suitable while for fine-grained localization task, i.e., 𝑚= 0.5 and 𝑚= 0.7, 𝛼= 0.3 is better. • The importance of frame-moment correspondence loss and moment-frame correspondence loss. In the Eqn. (17), 𝛽and 𝛾represent the importance of frame-moment correspon- dence loss and moment-frame correspondence loss in the final loss. We test different combinations of 𝛽and 𝛾in the range 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 ACM MM, 2024, Melbourne, Australia Anonymous Authors 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 Figure 4: The results of R1@0.7 from different frame- moment correspondence loss and moment-frame correspon- dence loss. Evaluation performed on the Charades-STA dataset. Query: She picks up the rinds and throws them away. Ground Truth 90.2 s 95.2 s VIGA Ours 86.4 s 98.1 s 96.4 s 88.7 s Figure 5: Visualization of localization results on TACoS. The yellow bar represents the ground truth temporal boundaries of the language query, the blue bar depicts the predicted boundaries of ViGA, and the green bar signifies the predicted boundaries of SG-SCI. {0.05, 0.1, 0.2, 0.5, 1}, and display the numerical relationship be- tween 𝑅1@0.7, as shown in Figure 4. Similar to the 𝛼in GCA, 𝛽and 𝛾also show an overall trend of first increasing and then decreasing, and the highest value is obtained at 𝛽= 𝛾= 0.1. The experimental results indicate that frame-moment correspondence loss and moment-frame correspondence loss have a significant impact on fine-grained boundary perception. • Sliding Window Size. Sliding window is designed to capture multi-granularity features via multi-scale sliding windows. In fact, monotonically increasing or decreasing the size of the slid- ing window would lead to degradation of the representation discrimination, thus affecting overall performance. For instance, in extreme cases, the sliding window may encompass information from either the entire video or just a frame. In such scenarios the model struggles to learn and comprehend these moments holisti- cally, leading to a decline in overall localization performance. We conducted ablation experiments on the Charades-STA dataset, with a default step size of 4. The specific experimental results are shown in Table 4. The results indicate that the best performance is achieved when the sliding window size is 8, indicating that the model needs a moderate sliding window size for sufficient learning during Scale Correspondence Learning. Table 4: Performance comparison on Charades-STA with different sliding window sizes, and ∗means the stride is the half of sliding window size. Size R1@0.3 R1@0.5 R1@0.7 mIoU 4 70.89 49.46 23.66 46.02 8 70.30 52.07 27.23 46.77 16 70.78 47.18 21.99 45.23 24 70.43 46.02 21.48 44.96 32 69.84 45.3 19.46 44.22 4∗ 70.86 49.46 23.31 45.91 16∗ 70.83 47.15 22.07 45.22 24∗ 70.46 45.86 21.64 44.70 32∗ 69.09 44.44 20.16 44.03 Table 5: Speed comparison on Charades-STA and TACoS be- tween ViGA [10] and SG-SCI, time is averaged. Charades-STA TACoS Method Train Inference Train Inference ViGA [10] 1.0x 1.0x 1.0x 1.0x SG-SCI 1.1x 1.2x 1.0x 1.2x 4.4.3 Inference speed. As Table 5 illustrated, during the training and inference phases, our runtime is almost identical to ViGA’s [10]. This is because our proposed GCA and SCL do not introduce sig- nificant computational complexity. As a result, we consider our methods to be competitive in terms of efficiency. 4.4.4 Quantitative Results. To more thoroughly examine the contri- butions of our proposed SG-SCI framework, we present an example illustrating the results of moment retrieval on the TACoS datasets. As is shown from the Figure 5, SG-SCI demonstrates superior effec- tiveness in terms of boundary perception. This can be attributed to the training process, where our model effectively enhances the model’s perceptual ability across different scales. 5 CONCLUSION AND FUTURE WORK In this paper, we designed a new framework to understand the cor- respondence connections between video and text. This framework used a limited amount of data from single frame to learn. Initially, we looked at the problems existing methods had in linking the mean- ing of video and text that varied in detail. To address these issues, our method included two main strategies: (1) creating a model that used underlying knowledge to connect features of different modal- ities, and (2) developing a learning strategy that focused on the differences in information between similar samples. These strate- gies helped our method to clearly define and find the differences in meaning across various details and modalities. This made our method better at representing features of explicit granularity and implicit scales. Experiments conducted on two benchmark datasets demonstrated the effectiveness of our proposed framework. In the future, in order to further improve the performance of VML, we plan to introduce a more advanced visual-language trans- former backbone. In addition, inspired by the fully-supervised par- adigm, we plan to study the impact of different inference methods on localization performance. 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 Explicit Granularity and Implicit Scale Correspondence Learning for Point-Supervised Video Moment Localization ACM MM, 2024, Melbourne, Australia 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 REFERENCES [1] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. 2017. Localizing moments in video with natural language. 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[54] Minghang Zheng, Yanjie Huang, Qingchao Chen, and Yang Liu. 2022. Weakly supervised video moment localization with contrastive negative sample mining. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 3517–3525. [55] Minghang Zheng, Yanjie Huang, Qingchao Chen, Yuxin Peng, and Yang Liu. 2022. Weakly supervised temporal sentence grounding with gaussian-based contrastive proposal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 15555–15564. IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023 6121 Point-Supervised Video Temporal Grounding Zhe Xu , Kun Wei , Xu Yang, Member, IEEE, and Cheng Deng , Senior Member, IEEE Abstract—Given an untrimmed video and a language query, Video Temporal Grounding (VTG) aims to locate the time interval in the video semantically relevant to the query. Existing fully- supervised VTG methods require accurate annotations of temporal boundary, which is time-consuming and expensive to obtain. On the other hand, weakly-supervised VTG methods where only paired videos and queries are available during training lag far behind the fully-supervised ones. In this paper, we introduce point supervision to narrow the performance gap with affordable annotating cost and propose a novel method dubbed Point- Supervised Video Temporal Grounding (PS-VTG). Speciﬁcally, an attention-based grounding network is ﬁrst employed to obtain a language activation sequence (LAS). Then pseudo segment- level label is generated based on the LAS and the given point supervision to assist the training process. In addition, multi- level distribution calibration and cross-modal contrast are framed to obtain discriminative feature representations and precisely highlight the language-relevant video segments. Experiments on three benchmarks demonstrate that our method trained with point supervision can signiﬁcantly outperform weakly-supervised approaches and achieve comparable performance with fully- supervised ones. Index Terms—Cross-modal contrast, multi-level distribution calibration, point supervision, video temporal grounding. I. INTRODUCTION W ITH the explosive growth of video data, video under- standing has attracted increasing research attention in the past few years. Traditional video understanding tasks, e.g., video classiﬁcation [1], [2], object tracking [3], [4], and action localization [5], [6] are limited in the video modality and pre- deﬁned action categories. Recently, Video Temporal Grounding (VTG) [7], [8], [9] was introduced to locate the time interval in an untrimmed video semantically relevant to a language query, which involves both the video and language modalities. Due to the requirement of the comprehensive understanding and pre- cise alignment of the multi-modal information, VTG is a more challenging and practical task. According to the supervision available during training, exist- ing VTG methods can be divided into two categories: fully- supervised VTG [7], [8], [10], [11] and weakly-supervised Manuscript received 21 May 2022; revised 7 August 2022 and 1 Septem- ber 2022; accepted 3 September 2022. Date of publication 9 September 2022; date of current version 1 November 2023. This work was supported in part by the National Natural Science Foundation of China under Grants 62132016, 62171343, and 62071361, in part by the Key Research and Development Pro- gramofShaanxiunderGrant2021ZDLGY01-03,andinpartbytheFundamental Research Funds for the Central Universities under Grant ZDRC2102. The As- sociate Editor coordinating the review of this manuscript and approving it for publication was Dr Xinxiao Wu. (Corresponding author: Cheng Deng.) The authors are with the School of Electronic Engineering, Xidian University, Xi’an 710071, China (e-mail: zhexu@stu.xidian.edu.cn; weikunsk@gmail.com; xuyang.xidian@gmail.com; chdeng.xd@gmail.com). Digital Object Identiﬁer 10.1109/TMM.2022.3205404 Fig. 1. Illustration of different levels of supervision for VTG. (1) For full su- pervision, the start and end time in a video corresponding to a language query are required to be annotated. (2) For weak supervision, annotators are only asked to identify whether the activity corresponding to a language query exists in a video or not. (3) For point supervision, only a single temporal point corresponding to a language query need to be annotated. VTG [9], [12], [13]. Speciﬁcally, fully-supervised VTG requires annotators to label the accurate start and end time in a video cor- responding to a language query. Despite the remarkable success, it is time-consuming and expensive to obtain such ﬁne-grained annotations. Thus, weakly-supervised VTG is proposed to re- duce the expensive cost of annotating temporal boundary. In this setting, annotators are only asked to identify whether the activity corresponding to a language query exists in a video or not. While weakly-supervised methods can signiﬁcantly reduce the annotation cost, their performances are apparently inferior to the fully-supervised ones. In order to narrow the performance gap with affordable cost, we are motivated to introduce an intermediate form supervision, i.e., point supervision, for VTG. Concretely, only a single tem- poral point of a video relevant to a language query needs to be an- notated in the point-supervised setting. Fig. 1 shows an example of different levels of supervision for VTG. Given an untrimmed video and a language query the person puts down the bag, full supervision needs annotators to watch the video repeatedly to determine the accurate start and end time corresponding to the query, which is time-consuming and labor-intensive. For weak supervision, annotators can only watch the video once to iden- tify whether the activity corresponding to the query exists in the video or not. The proposed point supervision requires negligible extra annotating cost compared with the weakly-supervised one, yet provides a coarse-grained location of the query. Since the point annotation is available during training, how to use it becomes an imperative problem for point-supervised VTG. Intuitively, maximizing the matching score between the annotated video point and the language query can be employed to train the model. The extreme sparsity of label, however, is likely to enforce the model only pay attention to the annotated 1520-9210 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information. Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. 6122 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023 video segments and thus leads to unsatisfying grounding per- formance, as shown in our experiments. Recently, pseudo la- bel generation methods [14], [15] have been employed in other point-supervised video tasks. But these methods are limited in video modality with pre-deﬁned categories and require anno- tated points belonging to different classes to determine the ac- tion boundary. In contrast, point-supervised VTG involves both video and language modalities with complex multi-modal in- teractions. How to locate the temporal boundary relevant to a language query under the point supervision is a more challeng- ing problem. In this paper, we present a novel method, dubbed PS-VTG, for point-supervised VTG. A language activation sequence (LAS) is ﬁrst obtained by an attention-based grounding network. Since there is no annotated points belonging to other classes avail- able for reference, segment-level pseudo labels are proposed to be generated in a “candidate + selecting” manner. Speciﬁ- cally, a set of candidate labels are generated based on the LAS and pre-deﬁned activity thresholds. Then the ﬁnal pseudo la- bel is selected after ﬁltering out the irrelevant and overlapping ones by the point supervision and Non-Maximum Suppression (NMS). In addition, cross-modal contrast and multi-level distri- bution calibration are introduced to further enforce the model to precisely highlight the language-relevant segments. Our key contributions can be summarized as follow: 1) To the best of our knowledge, we are the ﬁrst to introduce point supervision for VTG, which aims to narrow the per- formance gap between weakly- and fully-supervised VTG with affordable annotation cost. 2) We present a novel method for point-supervised VTG, where pseudo segment-level labels are generated based on the point supervision to effectively train the model. 3) We propose cross-modal contrast and multi-level distribu- tion calibration to precisely align the video and language, assisting to highlight the video segments relevant to the query. 4) Extensive experiments on three benchmark datasets demonstrate that our method can signiﬁcantly outperform the weakly-supervised approaches and achieve compara- ble performance with fully-supervised ones. II. RELATED WORK Fully-Supervised Video Temporal Grounding. Methods of fully-supervised VTG can be roughly divided into two cate- gories: (1) proposal-based methods, and (2) proposal-free meth- ods. Proposal-based methods [7], [8] follow the two-stage “pro- posal + matching” paradigm, where a set of candidate proposals are generated for a given video and then the most relevant time interval is selected. CTRL [7] uses a dense sliding window to produce activity proposals and proposes Cross-modal Temporal Regression Localizer to generate alignment scores and location regression results for candidate proposals. SAP [8] proposes a framework named Semantic Activity Proposal to integrate se- mantic information into the proposal generation process. Since proposal-based methods need to compare all the pro- posals with the language query, their computational costs are extremely expensive. Therefore, proposal-free methods [11], [16], [17], [18] are proposed to treat VTG as a regression prob- lem and directly predict the time interval. ABLR [16] introduces co-attention mechanism for VTG and proposes Attention Based Location Regression to regress the temporal coordinates of lan- guage from attention weights or attended features. LGI [11] ex- tractes multiple semantic phrases from a language query and presents Local Global Interactions to fuse multi-modal infor- mation. Despite the remarkable success, it is time-consuming and ex- pensive to obtain the temporal boundary annotations for fully- supervised VTG. Weakly-Supervised Video Temporal Grounding. Weakly- supervised VTG [9], [12], [19], [20], [21] is proposed to re- duce the expensive cost of annotating temporal boundary for fully-supervised VTG. TGA [9] presents Text-Guided Atten- tion to highlight video segments relevant to a language query and obtain a single text-dependent video feature. The network is trained by minimizing the distance between the text-dependent video feature and the language feature. VLANet [12] proposes Video-LanguageAlignmentNetworktopruneoutirrelevantpro- posals and consider various attention ﬂows to learn multi-modal alignment. Recently, CRM [19] introduces cross-sentence tem- poral and semantic consistency as constraints to mine the com- plex relations in videos. Strictly speaking, however, it is not a weakly-supervised method since temporal order is employed as an additional supervision. While weakly-supervised methods can signiﬁcantly reduce the annotation cost, their performances fall largely behind the fully-supervised ones. Point Supervision. In order to achieve a better trade-off be- tweentest performanceandtrainingannotationcost, point super- vision [14], [15], [22], [23], [24], [25], [26] has been introduced to some computer vision tasks. Bearman et al. [22] ﬁrst introduce point supervision for semantic segmentation. Mettes et al. [23] apply point supervision for spatio-temporal action localization in video. Recently, SF-Net [14] proposes to use single-frame supervision for video temporal action localization. Li et al. [15] present a temporal action segmentation model using only times- tamps annotations. In this paper, we introduce the point supervision for VTG. Different previous tasks which are limited in uni-modality and pre-deﬁned categories, VTG involves complex video and lan- guage interactions, determining PS-VTG to be more suitable for real-world situations. III. PROPOSED METHOD Given an untrimmed video V , a language query Q, and a point annotation tp, point-supervised VTG aims to learn a model f that can predict the time interval I = (ts, te) in the video cor- responding to the query: f : (V, Q, tp) →I. ts and te represent the start and end time, respectively. Notably, tp is a timestamp within I, i.e., ts ≤tp ≤te, and the only supervision available during training. Fig. 2 shows the framework of our proposed method. Video segment features and word-level language features are ﬁrst Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING 6123 Fig. 2. The framework of our proposed point-supervised VTG method. Given video segment features and word-level query features extracted from pre-trained models, a ﬁne-grained interaction framework is ﬁrst employed to fuse the multi-modal information. Then we obtain the language activation sequence (LAS) based on video segment features and pooled query feature. Pseudo label is generated by the point supervision and predicted LAS. In addition, cross-modal contrast and multi-level distribution calibration are proposed to precisely align the video and language. Fig. 3. Illustration of the proposed pseudo label generator. extracted from pre-trained video and language models, respec- tively. Then we fuse the video and language features with a ﬁne-grained interaction network to obtain the language acti- vation sequence (LAS). The elaborated label generator is em- ployed to generate segment-level pseudo label based on the point supervision and LAS. In addition, cross-modal contrast and multi-level distribution calibration are performed to better align the video with the language query. In the following, we elaborate the main components of the proposed method, i.e., (1) grounding network, (2) pseudo label generation, (3) cross-modal contrast, (4) multi-level distribution calibration, and (5) training and inference. A. Grounding Network Suppose Xi ∈RdV ×LV and Qi ∈RdQ×LQ (i = 1, 2, . . . , N) as the video segment features and query word features extracted from pre-trained video and language models, respectively. dV is the dimension of video segment features, dQ is the dimension of query word features, LV is the number of segments per video, LQ is the number of words per sentence, and N is the batch-size. We ﬁrst embed Xi and Qi into a com- mon space with dimension d. Then a ﬁne-grained interaction network are employed to obtain semantic-aware video segment features V i ∈Rd×LV and global query feature qi ∈Rd. Speciﬁcally, segment-wise query representation Si ∈Rd×LV is ﬁrst learned by attending word-level features based on each video segment feature: Si t = LQ l=1 λtlQi l, (1) where λtl is the normalized attention weight computed by the t-th segment feature, i.e, λtl = exp(rtl) LQ k=1 exp(rtk) and rtl = wT r tanh(WQQi l + WXXi t). WQ, WX ∈Rd×d and wT r are the learnable matrices. Then we fuse Si and Xi to obtain semantic-aware video segment features V i: V i t = WV((Xi t ⊙Si t)||Si t)), (2) where WV ∈Rd×2 d is a learnable embedding matrix. ⊙and || represent the element-wise multiplication and the concatenation of two features, respectively. In addition, the global query feature qi is obtained by weighted pooling: qi = QiSoftmax(WGQi)⊤, (3) where WG ∈R1×d is a learnable matrix. LAS ai is obtained based on V i and qi: ai t = Sigmoid(Wa(V i t||qi)), (4) where V i t (t = 1, 2, . . . , LV ) is the t-th segment feature of V i and ai t is the predicted activation value of V i t. || represents the concatenation of two features and Wa ∈R1×2 d is a learnable embedding matrix. Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. 6124 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023 B. Pseudo Label Generation Since point-annotation is available in the proposed point- supervised setting, how to use it to train the grounding model be- comes a imperative problem. In this paper, we present a pseudo label generator, as shown in Fig. 3, to generate segment-level pseudo label based on the point supervision and LAS. Given the predicted LAS ai, we ﬁrst generate a set of candi- dates according to pre-deﬁned activity thresholds. Speciﬁcally, a video clip is selected to be a candidate if the activation values within it consecutively greater than the threshold. In addition, a matching score is computed for each candidate. Taking a can- didate (ts, te) as an example, the matching score m is deﬁned as: m = 1 l te t=ts ai t − 1 LV −l ts−1 t=1 ai t + LV t=te+1 ai t , (5) where ts, te, and l = te −ts + 1 are the start index, end index, and length of the candidate, respectively. The ﬁrst and second terms represent the mean activation of the candidate and its tem- poral neighbors, respectively. Then the candidates that do not include the point annotation will be ﬁltered out. Non-Maximum Suppression (NMS) is per- formed to remove overlapping candidates and pseudo label p is obtained from the left candidates with greatest matching score: pi t = 1, ˆ ts ≤t ≤ˆ te 0, otherwise (t = 1, 2, . . . , LV ) , (6) where ˆ ts and ˆ te are the start and end index of the selected can- didate, respectively. The binary cross entropy loss is employed to enforce the pre- dicted ai to be close to the generated pseudo label pi: LBCE = 1 N N i=1 pi log ai + 1 −pi log 1 −ai . (7) Since the generated pseudo label is binary and includes the point supervision, the model trained with LBCE will gradually highlight the video segments semantically relevant to the lan- guage query. C. Cross-Modal Contrast In order to precisely align the video and language, we present cross-modal contrast based on the point supervision. Speciﬁ- cally, given a language query qi as an anchor, we select video segment vi including the point annotation as a positive sam- ple. Negative samples come from both the intra- and inter videos, i.e., unmatched segments determined by the pseudo label and segments corresponding to other queries in the mini-batch. Video2query contrastive loss LCON_V 2Q is employed to ob- tain discriminative representation by pulling the anchor and the positive sample together while pushing away the anchor from Fig. 4. Illustration of the optimal distribution of LAS and two local optimums. negative samples, which can be deﬁned as: LCON_V 2Q = −1 N N i=1 × log es(qi,vi)/τ es(qi,vi)/τ +N k̸=i es(qi,vk)/τ +M j=1 es(qi,vj)/τ , (8) where s(x, y) = x⊤y/||x||||y|| denotes cosine similarity and τ is the temperature parameter. N and M are the batch-size and the number of negative intra-video samples, respectively. Analogously, we deﬁne the query2video contrastive loss where video segment is treated as an anchor and queries as con- trastive samples: LCON_Q2V = −1 N N i=1 log es(vi,qi)/τ es(vi,qi)/τ + N k̸=i es(vi,qk)/τ. (9) The total cross-modal contrastive loss is the sum of video2query and query2video contrastive loss: LCON = LCON_V 2Q + LCON_Q2V . (10) D. Multi-Level Distribution Calibration Ideally, the distribution of predicted ai along time is plateau- like, i.e., the activation values of query-relevant segments in the middle are close to 1 while close to 0 otherwise. To this end, we propose multi-level distribution calibration to restrict the distribution of predicted language activation sequence and enforce the model to precisely focus on the relevant segments. For intra-video distribution calibration, the mean activation within the time interval of pseudo label is encouraged to be higher than that outside the interval. The intra-video distribution calibration loss is thus deﬁned as: LMDC_R = 1 N N i=1 max ⎛ ⎝0, ˆ ts−1 t=1 ai t + LV t= ˆ te+1 ai t LV −ˆ l − ˆ te t= ˆ ts ai t ˆ l + δ ⎞ ⎠, (11) where ˆ ts, ˆ te, and ˆ l = ˆ te −ˆ ts + 1 represent the start index, end index and length of the pseudo label, respectively. LV is the number of segments per video and δ is the margin (set to 0.5). Despite the ability of intra-video distribution calibration to make the activation within and outside the interval distinguish- able, the predicted ai is still likely to be local optimum. Con- cretely, the value of ai may close to 1 only at the annotated point or at every points, i.e., LV t=1 ai t = 1 or LV t=1 ai t = LV . Fig. 4 Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING 6125 shows the distribution of the optimal LAS (4a) and two local optimums (4b and 4c). To avoid the local optimums, we propose inter-video distribu- tion calibration. The mean activation of videos in a mini-batch is forced to subject a Gaussian distribution with middle value mean. The inter-video distribution calibration loss can be de- ﬁned as: LMDC_E = 1 N N i=1 LV t=1 ai t LV −μ + 1 N N i=1 LV t=1 ai t − LV t=1 ai t LV 2 LV −σ , (12) where the ﬁrst and the second terms of the loss function are employed to restrain the mean and variance of the predicted activations, respectively. μ and σ are the mean and variance of pre-deﬁned Gaussian distribution, respectively. As veriﬁed later in the experiments, a middle value mean will encourage the model to precisely focus on the segments relevant to the language query. The multi-level distribution calibration loss LMDC is the sum of intra- and inter-video distribution calibration loss: LMDC = LMDC_R + LMDC_E. (13) E. Training and Inference In summary, three loss functions are employed for training, namely: binary cross entropy loss LBCE, cross-modal con- trastive loss LCON, and multi-level distribution calibration loss LMDC. The overall objective function can be formulated as: L = LBCE + αLCON + βLMDC, (14) where α and β are the weighting coefﬁcients for the cross-modal contrastive loss and the multi-level distribution calibration loss, respectively. During inference, given an untrimmed video and a language query, we ﬁrst obtain the LAS by the grounding network. Then the ﬁnal grounding result is determined by a similar process to pseudo label generation without ﬁltering out candidates ir- relevant to point supervision, i.e., generating candidates then selecting top1 by NMS. IV. EXPERIMENTS In this section, the datasets and evaluation metrics we used are ﬁrst introduced in detail. Then, we present the implementation details and the simulation of point supervision of our method. Finally, the analysis, comparison with state-of-the-art methods, and qualitative results further prove the effectiveness of our pro- posed approach. A. Datasets We evaluate the proposed method on three public datasets: ActivityNet Captions [27], Charades-STA [7], and TACoS [28]. ActivityNet Captions. The ActivityNet Captions dataset contains 10,024, 4,926 and 5,044 videos for training, validation, and testing, respectively. The average length of language query and average number of activities per video are 13.48 words and 3.65, respectively. We use the validation set for evaluation since the testing set is not publicly available. Charades-STA. The Charades-STA dataset is built on Cha- rades dataset for video temporal grounding. It contains 12,408 and 3,720 clip-language pairs for training and testing, respec- tively. The average length of language query, average duration of videos and average number of activities per video are 8.6 words, 29.8 seconds, and 2.3 respectively. TACoS. The TACoS dataset is built on MPII-Compositive dataset and consists of 10,146, 4,589, and 4,083 instances for training, validation, and testing, respectively. The average dura- tion of videos is around 300 s. B. Evaluation Metrics Following previous works, we report two metrics to measure the performance of video temporal grounding: IoU = n and mIoU. Rank@1 IoU = n. Rank@1 IoU = n is referred to as the percentage of test samples whose Intersection over Union (IoU) with ground-truth (GT) is higher than n. n = {0.3, 0.5, 0.7} are reported in our experiments. mIoU. mIoU is the mean IoU over all test samples. C. Implementation Details We use pre-trained C3D [29], I3D [30], and C3D models to extract video features for the ActivityNet Captions, Charades- STA, and TACoS datasets, respectively. Each video is uniformly sampled into LV = 128 segments and embedded into a dV × LV dimensional representation space by an embedding matrix. Glove [31] is employed to extract word features of the language query. The dimensions of the video segment dV , the language query dQ, and the common space d are set to 1024, 300, and 128, respectively. Our approach is implemented with PyTorch [32] and opti- mized by ADAM [33] optimizer with a learning rate of 0.0001. We set the batch size N to 64 and training epoch to 200. The loss weights α and β are determined by the grid search and set to 0.1 and 0.1 respectively. In addition, the activity thresholds for generating candidates and the threshold in NMS for ﬁltering out overlapping are set to (0.4, 0.6, 0.1) and 0.4, respectively. The temperature parameter τ in cross-modal contrast is set to 1. μ and σ for inter-video distribution calibration are set to (0.3, 0.1), (0.3, 0.01), and (0.1, 0.01) on the ActivityNet Captions, Charades-STA, and TACoS datasets, respectively. D. Simulation of Point Supervision Since the datasets used for evaluation do not have point an- notations themselves, we simulate the point supervision based Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. 6126 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023 TABLE I PERFORMANCE COMPARISON WITH WEAKLY- AND FULLY-SUPERVISED COUNTERPARTS ON THE ACTIVITYNET CAPTIONS, CHARADES-STA, AND TACOS DATASETS on the existing boundary annotations. Following the strategy in [34], we generate two sets of point annotations, i.e., PS-U and PS-G, from different distributions. Speciﬁcally, given s and e as the start and end time corresponding to a language query, the point annotation belonging to PS-U is randomly sampled from the uniform distribution [s, e]. In addition, we randomly sample points from a Gaussian distribution with mean (s + e)/2 and standard deviation 1 s to form the set PS-G. E. Analysis Gains from the point supervision. To investigate the gains of point supervision, we compare our point-supervised method with its weakly- and fully-supervised counterparts. Speciﬁcally, the weakly-supervised model is trained only by cross-modal contrastive loss, where the video segments are pooled by the predicted language activation sequence to obtain a video fea- ture. For fully-supervised counterpart, we replace the gener- ated pseudo label with ground-truth one to train the model. Our point-supervised model is trained with the binary cross entropy loss LBCE, cross-modal contrastive loss LCON, and multi-level distribution calibration loss LMDC. Table I shows the results under different levels of supervi- sion on the ActivityNet Captions, Charades-STA, and TACoS datasets. Our model trained with point supervision outperform the weakly-supervised counterpart with a large margin, e.g., 32.17%, 27.20%, 14.47% and 21.26% in terms of IoU = 0.3, IoU = 0.5, IoU = 0.7, and mIoU on the ActivityNet Captions dataset.Inaddition,comparableresultswiththefully-supervised counterpart are achieved by the point-supervised model. Consid- ering that point supervision requires negligible extra annotation cost comparing with weak supervision, the effectiveness of point supervision is clearly veriﬁed in this experiment. The annotation cost of point supervision. Given an untrimmed video and the language query, full supervision needs annotators to watch the video repeatedly to determine the ac- curate start and end time corresponding to the query, which is time-consuming and labor-intensive. For weak supervision, annotators can only watch the video once to identify whether TABLE II THE ANNOTATION COST OF DIFFERENT SUPERVISIONS ON THE ACTIVITYNET CAPTIONS DATASET TABLE III EFFECTIVENESS OF PSEUDO LABEL GENERATION ON THE ACTIVITYNET CAPTIONS, CHARADES-STA, AND TACOS DATASETS. LCON, LCON + LBCE−P S, AND LCON + LBCE DIFFER IN THE USE OF POINT SUPERVISION the activity corresponding to the query exists in the video or not. The proposed point supervision requires only a few extra pauses to annotate a temporal point, which is negligible extra annotating cost compared with the weakly-supervised one. In order to illus- trate the differences among supervisions, we randomly annotate 100 instances of the ActivityNet Captions dataset. Table II shows the average annotating time of three supervi- sions. The average annotating time at full, point, and weak levels are 504 s, 185 s, and 180 s, while the mIoU are 43.73%, 41.49%, and 20.23%, respectively. The results proves our method obtains impressive improvement with little increase of annotation cost compared with weakly-supervised methods. Effectivenessofpseudolabelgeneration.Givenpointsuper- visionavailableduringtraining,howtouseitbecomestheimper- ative problem for point-supervised VTG. In this paper, we pro- vide a novel solution. i.e., generating segment-level pseudo label based on the point supervision and predicted LAS. To investi- gate the effectiveness of pseudo label generation, we compare our method with two baseline approaches in this experiment: (1) LCON and (2) LCON + LBCE−P S. Speciﬁcally, LCON is the baseline approach trained only with cross-modal contrastive loss. In this case, point supervision is used to select segment as contrastive samples from a video. The baseline approach LCON + LBCE−P S is trained with cross-modal contrastive loss and binary cross entropy loss only at the annotated point. In con- trast, our method is trained with cross-modal contrastive loss and binary cross entropy loss at all points, i.e., LCON and LBCE. As shown in Table III, our method trained with LCON and LBCE signiﬁcantly outperform the two baseline approaches on the ActivityNet Captions, Charades-STA, and TACoS datasets, which indicates the effectiveness of the proposed pseudo label generation. Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING 6127 Fig. 5. Loss and mIoU vs. iteration using the binary cross entropy LBCE on the Charades-STA dataset. TABLE IV INFLUENCE OF POINT SUPERVISION SIMULATION METHODS ON THE ACTIVITYNET CAPTIONS, CHARADES-STA, AND TACOS DATASETS. PS-U AND PS-G REPRESENT POINT ANNOTATIONS SIMULATED FROM DIFFERENT DISTRIBUTIONS In order to investigate the convergence of training LAS using the generated pseudo label. We train the model with only the bi- nary cross entropy LBCE and present the training loss as well as mIoU during iteration on the Charades-STA dataset. As shown in Fig. 5, the loss decreases and mIoU increases during the train- ing process, which veriﬁes the convergence of training LAS us- ing the generated pseudo label. We attribute the convergence to elaborated pseudo label generator: (1) The candidates are generated according to pre-deﬁned activity thresholds (set to (0.4:0.6:0.1) in the experiments), which guarantees the consec- utiveness of the activation values in a candidate. (2) The candi- dates that do not include the point supervision are ﬁltered out, which makes full use of the point information. (3) The pseudo label is set to be binary while the LAS is continuous. Minimizing LBCE will enforce the predicted LAS to be close to the gener- ated pseudo label and make the model gradually highlight the video segments semantically relevant to the language query. Inﬂuence of point supervision simulation methods. As dis- cussed in the last subsection, we simulate two sets of point- annotations, i.e., PS-U and PS-G, from different distributions. In this experiment, we aim to investigate the inﬂuence of the point supervision simulation methods. The models are trained with the binary cross entropy loss LBCE, cross-modal contrastive loss LCON, and multi-level distribution calibration loss LMDC on the ActivityNet Captions, Charades-STA, and TACoS datasets. Table IV presents the experimental results. Similar results are obtainedbasedonthetwosimulationmethods.Pointannotations from PS-U are empolyed in rest experiments. Contribution of multi-level distribution calibration. In or- der to verify the effectiveness of multi-level distribution calibra- tion, we present an ablation study on the ActivityNet Captions, TABLE V EFFECTIVENESS OF MULTI-LEVEL DISTRIBUTION CALIBRATION ON THE ACTIVITYNET CAPTIONS, CHARADES-STA, AND TACOS DATASETS. MDC_R, MDC_E, AND MDC REPRESENT THE INTRA-VIDEO DISTRIBUTION CALIBRATION, INTER-VIDEO DISTRIBUTION CALIBRATION, AND OVERALL MULTI-LEVEL DISTRIBUTION CALIBRATION, RESPECTIVELY TABLE VI CHOICE OF μ AND σ ON THE ACTIVITYNET CAPTIONS DATASET Charades-STA,andTACoSdatasets.Fourvariantsofourmethod are compared in this experiment: (1) PS-VTG, (2) w/o MDC_R, (3) w/o MDC_E, and (4) w/o MDC. Speciﬁcally, PS-VTG is our full model trained with the binary cross entropy loss LBCE, cross-modal contrastive loss LCON, and multi-level distribu- tion calibration loss LMDC. w/o MDC_R, w/o MDC_E, and w/o MDC are the full model without intra-video distribution calibra- tion, inter-video distribution calibration, and overall multi-level distribution calibration, respectively. We show the experimental results in Table V. The perfor- mance largely drops when the intra-video or inter-video distri- bution calibration is removed, which indicates the effectiveness of multi-level distribution calibration. Choice of μ and σ. Inter-video distribution calibration is pro- posed to force the mean activation of videos in a mini-batch sub- ject a Gaussian distribution with middle value mean. We perform experiments with different Gaussian distribution to determine the optimal mean μ and variance σ of the Gaussian distribution. Table VI shows the results on the ActivityNet Captions dataset. A middle mean can bring about different improvements andtheoptimalperformanceisobtainedwhenμ = 0.3, σ = 0.1. F. Comparison With State-of-The-Art Methods We compare the proposed method with several state-of-the- art methods at different levels of supervison. Among these methods, TGA [9], WSLLN [35], VLANet [12], SCN [20], RTBPN [13], BAR [21], LCN [36], and LoGAN [45] are Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. 6128 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023 weakly-supervised methods. CRM [19], as discussed in re- lated works, requires additional temporal order supervision and thus is a special weakly-supervised approach. While CTRL [7], SAP[8],MLVI[37],TripNet[38],RWM[39],DEBUG[40],2D- TAN [10], ABLR [16], GDP [17], VSLNet [41], LGI [11], DRN [42], PMI-LOC [18], IVG-DCL [43] and MMN [44] are fully-supervised ones. To make the presented paper self- contained,wegiveabriefintroductionofthemethodsmentioned above. r TGA presents Text-Guided Attention to highlight video segments relevant to a language query and obtain a single text-dependent video feature. The network is trained by minimizing the distance between the text-dependent video feature and the language feature. r WSLLN proposes a two-branch network to measure segment-text consistency and conduct segment selection (conditioned on the text) simultaneously. r VLANet proposes Video-Language Alignment Network to prune out irrelevant proposals and consider various atten- tion ﬂows to learn multimodal alignment. r SCN proposes a Semantic Completion Network to measure the semantic similarity between proposals and query. r RTBPN presents a Regularized Two-Branch Proposal Net- work for weakly-supervised VTG. A language-aware ﬁlter is employed to generate the enhanced and suppressed video streams to generate positive and negative proposals. r BAR proposes Boundary Adaptive Reﬁnement framework that resorts to reinforcement learning to guide the process of progressively reﬁning the temporal boundary. r LCN proposes a Local Correspondence Network which consists of hierarchical feature representation, cycle- consistent local correspondence, and relation modeling among candidate moments to explore the ﬁne-grained cor- respondences between video and text. r CRM introduces cross-sentence temporal and semantic consistency as constraints to mine the complex relations in videos, where temporal order is employed as an addi- tional supervision. r CTRL is a pioneering VTG work. It leverages sliding win- dowtoobtaincandidateclipsofvariouslengthsandfusethe candidate representations with the sentence representation by three operators (i.e.,add, multiply, and fully-connected layer) to predict the alignment score. r SAP proposes a Semantic Activity Proposal framework that integrates the semantic information of sentence queries into the proposal generation process to get discriminative activity proposals. Visual and semantic information are jointly utilized for proposal ranking and reﬁnement. r MLVI introduces a multilevel feature integration model to fuse language and vision earlier and more tightly. r MAN presents a Moment Alignment Network where lan- guage query is integrated as dynamic ﬁlters. In addition, an iterative graph adjustment network is devised to model moment-wise temporal relations. r TripNet introduces an end-to-end reinforcement learning framework that uses a gated-attention mechanism over cross-modal features. TABLE VII PERFORMANCE COMPARISON WITH STATE-OF-THE-ART METHODS AT DIFFERENT LEVELS OF SUPERVISION ON THE ACTIVITYNET CAPTIONS DATASET. * INDICATES ADDITIONAL SUPERVISION IS EMPLOYED r RWM views VTG as controlling an agent to read the de- scription, to watch the video as well as the current localiza- tion, and then to move the temporal grounding boundaries iteratively to ﬁnd the best matching clip. r 2D-TAN proposes to model the temporal relations between video candidates by a two-dimensional map, where one dimension indicates the starting time of a moment and the other indicates the end time. The 2D temporal map can cover diverse video moments with different lengths, while representing their adjacent relations. r ABLR proposes a Attention Based Location Regression model to directly regress the temporal coordinates from the global attention outputs with a multi-modal co-attention mechanism. r GDP designs a novel bottom-up model: Graph-FPN with Dense Predictions. It ﬁrst generates a frame feature pyra- mid to capture multi-level semantics, then utilizes graph convolution to encode the plentiful scene relationships. r VSLNet proposes a video span localizing network on top of the standard span-based QA framework with query-guided highlighting strategy. r LGI introduces a sequential query attention module to extract representations of multiple and distinct semantic phrases from a text query. Then Local-Global video-text Interaction algorithm is employed to model the relationship between video segments and semantic phrases in multiple levels. r DRN proposes a Dense Regression Network for VTG, which provides a new perspective to leverage dense su- pervision from the sparse annotations. r PMI-LOC proposes pairwise modality interaction in both the sequence and channel levels to better understand video contents. Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING 6129 Fig. 6. The qualitative results of different models on the Charades-STA and ActivityNet Captions datasets. GT is the ground-truth time interval. Ours, w/o MDC, and Base Model represent the proposed PS-VTG model, PS-VTG without multi-level distribution calibration, and model without pseudo label generation, respectively. TABLE VIII PERFORMANCE COMPARISON WITH STATE-OF-THE-ART METHODS AT DIFFERENT LEVELS OF SUPERVISION ON THE CHARADES-STA DATASET. * INDICATES ADDITIONAL SUPERVISION IS EMPLOYED r IVG-DCL proposes interventional video grounding to eliminate the spurious correlations between query and video features based on causal inference. In addition, a dual contrastive learning approach is employed to better align the text and video. TABLE IX PERFORMANCE COMPARISON WITH STATE-OF-THE-ART METHODS AT DIFFERENT LEVELS OF SUPERVISION ON THE TACOS DATASET r MMN presents a Mutual Matching Network on a perspec- tive of metric-learning to directly model the similarity between language queries and video moments in a joint embedding space. Tables VII, VIII, and IX show the results on the ActivityNet Captions, Charades-STA, and TACoS datasets, respectively. Our approach outperforms all the weakly-supervised methods and achieve comparable results with fully-supervised ones. For ex- ample, BAR is the best weakly-supervised VTG method on the ActivityNet Captions dataset and our approach outperforms it with a large margin of 10.68% and 8.86% in terms of IoU = 0.3 and IoU = 0.5, respectively. In addition, our approach trained with point-supervision can even outperform some recent fully- supervised methods, e.g., CTRL, MLVI, TripNet, RWM, ABLR, Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply. 6130 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023 DEBUG, GDP, LGI, and PMI-LOC on the ActivityNet Captions dataset. G. Qualitative Results In Fig. 6, we show the qualitative results of different mod- els. Our proposed model PS-VTG achieves more accurate video temporal grounding than w/o MDC without multi-level distribu- tion calibration and Base Model without pseudo label generation (i.e., trained with cross-model contrastive loss and binary cross entropy loss only at annotated point), demonstrating the effec- tiveness of our method. V. CONCLUSION AND FUTURE WORK In this paper, we introduce point supervision for VTG, which can effectively narrow the performance gap between weakly- and fully-supervised VTG with affordable annotation cost. To make full use of the point supervision, we generate pseudo segment-level labels based on the point supervision and the pre- dicted language activation sequence. In addition, cross-modal contrast and multi-level distribution calibration are proposed to precisely align the video and language. Extensive experiments on three benchmark datasets demonstrate that our method can signiﬁcantly outperform the weakly-supervised approaches and achieve comparable performance with fully-supervised ones. Existing VTG methods rely on large-scale labeled data to train a model, which is time-consuming and expensive to obtain. In the future, we will explore to solve the VTG problem under the limited data [46], [47] and the limited supervision settings [48], [49], e.g., Semi-Supervised VTG, and Unsupervised VTG. REFERENCES [1] L.-Y. Duan, M. Xu, Q. Tian, C.-S. Xu, and J. S. Jin, “A uniﬁed framework for semantic shot classiﬁcation in sports video,” IEEE Trans. Multimedia, vol. 7, no. 6, pp. 1066–1083, Dec. 2005. [2] J. Wang, W. Wang, and W. 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Graf, “Tripping through time: Efﬁcient localization of activities in videos,” 2019. [Online]. Avail- able: http://arxiv.org/abs/1904.09936 [39] D. He et al., “Read, watch, and move: Reinforcement learning for tempo- rally grounding natural language descriptions in videos,” in Proc. AAAI Conf. Artif. Intell., 2019, vol. 33, pp. 8393–8400. [40] C. Lu, L. Chen, C. Tan, X. Li, and J. Xiao, “Debug: A. dense bottom- up grounding approach for natural language video localization,” in Proc. Empirical Methods Natural Lang. Process., 2019, pp. 5144–5153. [41] H. Zhang, A. Sun, W. Jing, and J. T. Zhou, “Span-based localizing net- work for natural language video localization,” in Proc. 58th Annu. Meeting Assoc. Comput. Linguistics, 2020, pp. 6543–6554. [42] R. Zeng et al., “Dense regression network for video grounding,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2020, pp. 10287–10296. [43] G. Nan et al., “Interventional video grounding with dual contrastive learn- ing,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2021, pp. 2765–2775. [44] Z. Wang, L. Wang, T. Wu, T. Li, and G. Wu, “Negative sample matters: A renaissance of metric learning for temporal grounding,” in Proc. AAAI Conf. Artif. Intell., 2022, vol. 36, pp. 2613–2623. [45] R. Tan, H. Xu, K. Saenko, and B. A. Plummer, “Logan: Latent graph co-attention network for weakly-supervised video moment retrieval,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., 2021, pp. 2083–2092. [46] K. Wei, M. Yang, H. Wang, C. Deng, and X. Liu, “Adversarial ﬁne-grained composition learning for unseen attribute-object recognition,” in Proc. Int. Conf. Comput. Vis., 2019, pp. 3741–3749. [47] X. Li, Z. Xu, K. Wei, and C. Deng, “Generalized zero-shot learning via dis- entangled representation,” in Proc. AAAI Conf. Artif. Intell., 2021, vol. 35, pp. 1966–1974. [48] J. Dong, Y. Cong, G. Sun, Z. Fang, and Z. Ding, “Where and how to trans- fer: Knowledge aggregation-induced transferability perception for unsu- pervised domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell., early access, Nov. 16, 2021, doi: 10.1109/TPAMI.2021.3128560. [49] J. Dong, Y. Cong, G. Sun, B. Zhong, and X. Xu, “What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2020, pp. 4022–4031. Zhe Xu received the B.E. degree in 2019 from Xi- dian University, Xi’an, China, where he is currently working toward the Ph.D. degree with the School of Electronic Engineering. His research interests include computer vision and machine learning. Kun Wei received the B.E. and Ph.D. degrees in electronic and information engineering from Xidian University, Xi’an, China, in 2017 and 2022, respec- tively. He is currently a Lecturer with the School of Electronic Engineering, Xidian University. His re- search interests include computer vision and machine learning. Xu Yang (Member, IEEE) received the B.E. and Ph.D. degrees in electronic and information engineer- ing from Xidian University, Xi’an, China, in 2021. He is currently a Lecturer with the School of Electronic Engineering, Xidian University. His research inter- ests include computer vision and machine learning. Cheng Deng (Senior Member, IEEE) received the B.E., M.S., and Ph.D. degrees in signal and informa- tion processing from Xidian University, Xi’an, China. He is currently a Full Professor with the School of Electronic Engineering, Xidian University. Dr. Deng is author or coauthor of more than 100 scientiﬁc arti- cles at top venues, including IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, IEEE TRANSACTIONS ON IM- AGE PROCESSING, IEEE TRANSACTIONS ON CYBER- NETICS, NeurIPS, ICML, CVPR, ICCV, AAAI, IJCAI, and KDD. His research interests include computer vision, pattern recognition, and information hiding. Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply.

Plain-text mathematical notation (without MathML)

Video Moment Retrieval from Text Queries via
Single Frame Annotation
Fudan University, China
ygj@fudan.edu.cn
ABSTRACT
Video moment retrieval aims at finding the start and end times-
tamps of a moment (part of a video) described by a given natural
language query. Fully supervised methods need complete temporal
boundary annotations to achieve promising results, which is costly
since the annotator needs to watch the whole moment. Weakly
supervised methods only rely on the paired video and query, but
the performance is relatively poor. In this paper, we look closer
into the annotation process and propose a new paradigm called
“glance annotation”. This paradigm requires the timestamp of only
one single random frame, which we refer to as a “glance”, within
the temporal boundary of the fully supervised counterpart. We
argue this is beneficial because comparing to weak supervision,
trivial cost is added yet more potential in performance is provided.
Under the glance annotation setting, we propose a method named
as Video moment retrieval via Glance Annotation (ViGA)1 based
on contrastive learning. ViGA cuts the input video into clips and
contrasts between clips and queries, in which glance guided Gauss-
ian distributed weights are assigned to all clips. Our extensive
experiments indicate that ViGA achieves better results than the
state-of-the-art weakly supervised methods by a large margin, even
comparable to fully supervised methods in some cases.
CCS CONCEPTS
• Computing methodologies →Visual content-based index-
ing and retrieval.
∗Both authors contributed equally to this research.
†Corresponding authors.
1We release our codes and glance re-annotated datasets at https://github.com/r-
cui/ViGA.
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SIGIR ’22, July 11–15, 2022, Madrid, Spain.
© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-8732-3/22/07...$15.00
https://doi.org/10.1145/3477495.3532078
KEYWORDS
video moment retrieval; contrastive learning; cross-modal learning
ACM Reference Format:
Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei
Guo, Huyang Sun, and Yu-Gang Jiang. 2022. Video Moment Retrieval from
Text Queries via Single Frame Annotation. In Proceedings of the 45th Inter-
national ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR ’22), July 11–15, 2022, Madrid, Spain. ACM, New York, NY,
USA, 11 pages. https://doi.org/10.1145/3477495.3532078
1
INTRODUCTION
Video moment retrieval (VMR), initially proposed in [1, 12], is the
task of retrieving the segment described by a given natural lan-
guage query from an untrimmed video. This task, also known as
natural language video localization [25, 48, 52] and video temporal
grounding [5, 29], is a fundamental problem in computer vision
understanding and visual information retrieval. Differing to an ear-
lier task video action localization [11, 22], which aims at localizing
pre-defined categorized actions from the video, VMR is considered
as a more difficult task since the query is generalized to free natural
language thus involving with more complex cross-modal contents
understanding. VMR can be widely applied in many scenarios such
as video browsing websites and semantics based video search en-
gines.
To date, deep learning methods have approached VMR from two
directions differing in the way of data annotation. In building a
dataset of fully supervised VMR, given the target video, the annotator
is asked to choose a segment in the video and write a short text
query to describe the segment. In the meanwhile, the start and end
timestamps of this segment are noted down. Thus, one example in
the dataset is a quadruplet of video, query, start and end, denoted by
(𝑉,𝑄,𝑠𝑡,𝑒𝑑). Though many methods under fully supervised VMR [8,
12, 29, 42, 45, 48, 49] have achieved good performance, an obvious
disadvantage of this data annotating paradigm is its high time cost.
Besides, the annotation quality varies according to the annotator’s
subjective judgements, especially in determining the start and end:
the annotator is forced to give specific timestamps of the query, but
the video segment is often not separated to its context with clear
border. For example, to annotate the start and end of a query “the
man turns the light on”, one might consider the very second that
the switch is toggled should be the temporal interval, but another
arXiv:2204.09409v3 [cs.CV] 18 Jun 2022

Query: He flips the eggs, making an omelette.
Weakly Supervised
Annotation
Fully Supervised
Annotation
Glance Annotation
0 s
198.1 s
114.9 s
156.5 s
138.3 s
Figure 1: Illustration of different VMR training example an-
notation paradigms. The fully supervised setting marks the
start and end timestamps of the moment corresponding to
query. Weak supervision only annotates the video-text pair.
Our proposed glance annotation marks a single timestamp
in the moment.
might start the annotation from when the man walks towards
the switch. This makes fully supervised VMR prone to subjective
annotation noise. To avoid these disadvantages, weakly supervised
VMR is proposed [28], in which one example in the dataset is simply
(𝑉,𝑄), and no start nor end annotation is available. Though not
comparable to fully supervised VMR in performance, many studies
[17, 23, 27, 37, 39, 44] have shown that weak supervision is a feasible
compromise when the annotating resources are limited.
In our study, we argue that weak annotation can be augmented
with trivial cost and propose “glance annotation”, a new paradigm
of data annotation in VMR. As illustrated in Figure 1, a training
example under glance annotation is composed of (𝑉,𝑄,𝑔) in which
𝑔is any timestamp between 𝑠𝑡and 𝑒𝑑. This paradigm is motivated
by the fact that to annotate even a weak example, it is still inevitable
for one to watch the video in order to write the query, and very
often it is sufficient to know what the moment is about by watching
only a short snippet of it. Assuming that with properly designed
graphical user interface to support the annotation, one can note
down an instant timestamp 𝑔during “glancing” the video with no
more effort than a mouse click. Glance annotation not only largely
saves the time consumption in watching the video compared to
full supervision, but also provides more information than weak
supervision.
To validate the usability of glance annotation, we re-annotate
three publicly available fully supervised VMR datasets, namely
ActivityNet Captions [20], Charades-STA [12] and TACoS [33] by
substituting 𝑠𝑡and 𝑒𝑑with a uniformly sampled timestamp 𝑔in
range [𝑠𝑡,𝑒𝑑]. Under this setting, we propose a contrastive learning
based method named Video moment retrieval via Glance Annotation
(ViGA). Due to the lack of 𝑠𝑡and 𝑒𝑑, ViGA follows the multiple-
instance learning (MIL) strategy widely adopted in weakly super-
vised VMR, which uses the correspondence between 𝑉and 𝑄as the
supervision signal. In doing the training, the main objective is to
obtain a proper cross-modal encoder to project 𝑉and 𝑄to a joint
embedding space, which satisfies that the distance between the
embeddings of corresponding (𝑉,𝑄)pos is closer and the distance
between the embeddings of other combinations (𝑉,𝑄)neg is farther.
ViGA extends this idea by splitting 𝑉into multiple clips 𝐶and
learning in the granularity (𝐶,𝑄) instead, for making use of 𝑔by
enabling an assignment of different weights to all clips. Specifically,
we use heuristic Gaussian distribution peaking at the glance po-
sition to generate the weights. In doing the inference, we follow
the common proposal-based inference as in many weakly super-
vised methods, yet adjust the classical sliding window proposal
generation to an anchor driven proposal generation to better fit our
training strategy. To be specific, those sliding window proposals not
including a first selected anchor frame are pruned out. We enable
the network finding the anchor by adding an additional training
objective of focusing the attention of our multihead-attention [41]
based backbone to the glance position.
As will be shown in the rest of this paper, ViGA significantly
outperforms the state of the art of weakly supervised VMR, even
comparable to fully supervised VMR when a high precision of re-
trieved segment is not required. Our contributions are summarized
as follows:
• We propose glance annotation, a new VMR annotating para-
digm, which requires no more significant annotating effort
than weakly supervised VMR, while provides more potential
regarding the retrieval performance.
• We propose a contrastive learning based method ViGA for
glance annotated VMR, which achieves significantly better
performance than weakly supervised methods.
• We release a unified version of glance annotations on top of
three publicly available datasets ActivityNet Captions [20],
Charades-STA [12] and TACoS [33], to encourage future
research on this topic.
2
RELATED WORK
After initially proposed by [1, 12], early VMR studies mostly use
the annotated start and end timestamps for the video-text temporal
alignment learning, which we term as fully supervised VMR [1, 5, 6,
12, 18, 24, 29, 49]. Due to the expensive annotation cost, researchers
then began to exploit on learning under weak annotation with
video-text pairs only, which we term as weakly supervised VMR
[10, 13, 17, 23, 27, 28, 51].
2.1
Fully Supervised VMR
Existing fully supervised VMR methods can be categorized into two
groups. Two-stage methods [1, 12, 14, 18, 19, 24, 45] typically gen-
erate some pre-segmentation of proposal candidates using a sliding
window or other proposal networks, then input the generated pro-
posals and the text query separately into a cross-modal matching
network to predict matching confidence and select the best match-
ing segment as the output. Hendricks et al. [1] first proposed Mo-
ment Context Network (MCN), which generated proposals based on
sliding window, and then projected the video moment feature and
text query feature into a common representation space. Then they
used 𝐿2 distance as a measure to optimize triplet loss to narrow the
distance of positive samples and enlarge the distance of intra-video
and inter-video negative samples. Xu et al. [45] proposed Query-
guided Segment Proposal Network (QSPN) for alleviating the huge
computation burden caused by sliding window. Specifically, QSPN
integrated query features into video features to obtain attention
weights in time indexing, then combined it with 3D Region of In-
terest (ROI) pooling to obtain the sparse proposals. End-to-end
models [5, 6, 29, 43, 46, 47, 49] can be divided into anchor-based

Query: He flips
the eggs, making
an omelette.
3DCNN
GloVe
max pooling
QAG-KL + GLS-NCE
Cross-modal Representation Module
Gaussian Alignment Module
Bi-GRU
Query Encoder
Query to Video
Cross-modal Encoder
Video to Query
Cross-modal Encoder
Self-attention
Video Encoder
glance
glance
Figure 2: Illustration of our training framework. In the Gaussian Alignment Module, a blue square denotes a word feature and
a yellow rectangle denotes the feature of one video frame. We use different heights of yellow squares to illustrate the different
weights of the frames. The solid and dashed green curves represent the heuristic Gaussian distribution generated with the
glance and the attention distribution generated by the model, respectively.
[5, 32, 43, 49] methods and anchor free [6, 7, 29, 46, 48] methods,
in which they differ in using / not using proposals in prediction,
respectively. As a typical work in the anchor-based category, Zhang
et al. [49] proposed 2D Temporal Adjacent Networks (2D-TAN) that
modeled the relations between segments of varying durations using
a two-dimensional feature map. The (𝑖, 𝑗)-th location of the feature
map indicated the start and end timestamps of the proposed seg-
ments. It then employed a Convolutional Neural Network (CNN) to
model the contextual interaction between various segments, using
ground truth labels to optimize the prediction score of each sug-
gestion in the feature map. For anchor-free methods, they usually
predict the probability of a frame being the start or end, or use a
neural network to directly regress the values of start and end. For
example, Lei et al. proposed XML [21] and used the 1D Convolu-
tional Start-End detector (ConvSE) to generate the start and end
scores on the late fused query-clip similarity matrix.
2.2
Weakly Supervised VMR
Although the fully supervised methods achieve good performance,
the expensive cost of annotating the temporal boundary limits
practical applications. Therefore, researchers recently began to pay
attention to the weakly supervised VMR [10, 13, 17, 23, 27, 28, 51].
Under the weakly supervised setting, we cannot obtain the detailed
start and end annotation of each query, only know whether the
query and video is a positive pair during training stage. Under this
constraint, most methods adopt the MIL framework. In MIL-based
VMR, the model learns the video-text alignment at video-level by
maximizing similarity scores of positive examples and suppress-
ing them on negative examples. Text-Guided Attention (TGA) [28]
was a typical pioneer work under the weak setting, which learned
text-aware video representation and leverages ranking loss to distin-
guish positive and negative samples. Ma et al. proposed VLANet [27]
which attempted to eliminate some irrelevant suggestions in the pro-
cess of MIL. Cross-sentence Relations Mining (CRM) [17] presented
by Huang et al. explored the temporal information modeling in
MIL using combinational associations among sentences. Semantic
Completion Network (SCN) [23] provided another reconstruction-
based idea of restoring the masked keywords in query according to
visual proposal and context information for the alignment learning
between modalities. Although weakly supervised VMR greatly re-
duces the burden of annotation, the performance of weak method
has a significant gap between the fully supervised method on the
test set.
3
METHODOLOGY
In this section, we first formally define the problem of glance an-
notated VMR and give an overview of our method ViGA. We then
introduce the two modules which form our training pipeline in
Section 3.3 and 3.4, respectively. The inference process is detailed
in Section 3.5.
3.1
Glance Annotation
Given an untrimmed video 𝑉and a text query 𝑄that semantically
describes a segment of the video, the VMR task aims at finding
the start and end timestamps 𝑠𝑡and 𝑒𝑑, such that moment 𝑉𝑠𝑡:𝑒𝑑
best corresponds to the query description. In fully supervised VMR,
complete human annotated 𝑠𝑡and 𝑒𝑑information is provided. In
contrast, under the weakly supervised VMR setting, only aligned
(𝑉,𝑄) pairs are available, with no fine-grained 𝑠𝑡or 𝑒𝑑information.
Our glance annotation scenario lies in between: a single timestamp
𝑔, satisfying 𝑠𝑡≤𝑔≤𝑒𝑑, is available at the training stage. We refer
to this timestamp 𝑔as a “glance”.
3.2
Algorithm Overview
Similar to the weakly supervised setting, it is not possible to let a
network learn to directly output 𝑠𝑡and 𝑒𝑑under glance annotation,
due to the lack of complete supervision signals. Instead, our method
selects a clip 𝐶from 𝑉that best matches 𝑄from a set of proposals
as the output. To learn this visual-textual alignment, many studies
in weakly supervised VMR adopt the MIL strategy and turn into
exploiting the correspondence of (𝑉,𝑄). Videos and queries that

we know are from the same example are marked as positive cor-
respondence (𝑉,𝑄)pos, while all other combinations in the batch
are treated as negative (𝑉,𝑄)neg. Our work extends this idea to
a finer-grained (𝐶,𝑄) level. Specifically, we build a network that
projects inputs from textual and visual modalities to a joint embed-
ding space, and train the network with a clip-to-query contrastive
objective, which pulls the distance between (𝐶,𝑄)pos closer and
pushes the distance between (𝐶,𝑄)neg farther.
Training. The overall structure of our training pipeline is il-
lustrated in Figure 2. After an initial feature extraction from pre-
trained models, our Cross-modal Representation Module encodes the
two input modalities by first applying two independent uni-modal
encoders, and then cross-interacting the two uni-modal features
to each other. As a result, token-level (words for text and frames
for video) cross-modal features are obtained. The Query to Video
Cross-modal Encoder additionally outputs an attention distribution
across all video frames. To train the network, we propose a Gauss-
ian Alignment Module, in which we generate a heuristic Gaussian
distribution peaking on the glance timestamp. All video frame fea-
tures are weighted by this heuristic distribution in calculating our
Gaussian Label-Smoothed Noise Contrastive Estimation loss (GLS-
NCE). The same Gaussian heuristic distribution is further used
in our Query Attention Guide Kullback–Leibler Divergence loss
(QAG-KL) to guide the learning of our network. The total loss of
our network is a fusion of the two loss functions.
Inference. To align with the training design, we propose a cor-
responding Query Attention Guided Inference strategy. After the
network forward pass up to the Cross-modal Representation Module,
the frame that gathers the most attention in Query to Video Cross-
modal Encoder is marked as the anchor frame. We sample proposals
of different sizes around this anchor frame (i.e., a proposal must
contain the anchor frame) and form a pool of proposals. The pro-
posal that gets the highest dot-product similarity to the sentence
feature is selected as the final output.
3.3
Cross-modal Representation Module
Given a video𝑉= [𝑣1, ..., 𝑣𝐿𝑣], and a query 𝑄= [𝑞1, ...,𝑞𝐿𝑞], we en-
code deep features of the two inputs using the proposed Cross-modal
Representation Module. Specifically, we first use two independent
encoders to ensure sufficient understanding of uni-modal semantics
for video and query. Next, to enable the cross-modal learning, we
fuse the semantics of the two modalities in the subsequent bidirec-
tional cross-modal encoder. As a result, per-token representations
v ∈R𝐿𝑣×𝑑model and q ∈R𝐿𝑞×𝑑model are obtained, where 𝑑model is
the dimension of the joint embedding feature (and also the overall
hidden dimension of our network).
Query Encoding. A bidirectional Gated Recurrent Unit (GRU)
is applied to encode the sequential semantics of all 𝐿𝑞words in 𝑄,
taking word embeddings from the pre-trained GloVe [31] model as
input. A word-level feature q𝑖is the concatenation of the forward
and backward hidden states of the final layer of the GRU, given by
q𝑖= [
→
h𝑖;
←
h𝑖] ∈R𝑑model.
(1)
·
·
·
Someone puts onions
in the skillet and beats
eggs into a bowl
before adding them to
the skillet.
The coach helps the
guy in red with the
proper body placement
and lifting technique.
A band plays music on
stage.
·
·
·
·
·
·
Figure 3: Illustration of clip-level MIL training strategy in
one batch in the Gaussian Alignment Module. Green solid
lines indicate positive correspondences, and gray dashed
lines indicate negative matching.
Video Encoding. For an untrimmed video, we first extract fea-
tures using a pre-trained CNN, such as C3D [40], I3D [4] and VGG
[36], followed by a fully connected layer to map the feature dimen-
sion to 𝑑model. To encode the sequential semantics of the extracted
video feature v, we apply a multihead self-attention module [41]
across all the frames. The encoding at the 𝑖-th frame is given by
Attn(𝑄(v𝑖), 𝐾(v),𝑉(v)) = softmax(𝑄(v𝑖)𝐾(v)𝑇
√︁
𝑑model/ℎ
)𝑉(v),
(2)
where 𝑄(·), 𝐾(·) and 𝑉(·) are three independent linear transforma-
tions from 𝑑model to 𝑑model, and ℎdenotes the number of heads.
Cross-modal Encoding. To fuse the information from the two
modalities, we apply cross-modal multihead attention after the
individual uni-modal self encoding, i.e., using one modality as query
and the other as key and value. In this way, the cross-encoding of
the 𝑖-th word is given by
Attn(𝑄(q𝑖), 𝐾(v),𝑉(v)) = softmax(𝑄(q𝑖)𝐾(v)𝑇
√︁
𝑑model/ℎ
)𝑉(v),
(3)
and the cross-encoding of the 𝑖-th frame is given by
Attn(𝑄(v𝑖), 𝐾(q),𝑉(q)) = softmax(𝑄(v𝑖)𝐾(q)𝑇
√︁
𝑑model/ℎ
)𝑉(q).
(4)
For each encoding module in the uni-modal encoding and the
cross-modal encoding, the module is subsequently followed by a
two-layer feed-forward module activated by ReLU [30] to further
enhance the encoding capacity. Moreover, we follow the standard
configuration of multihead attention modules, where layernorm
[2], dropout [38], position embedding [9] and residual connection
[16] are applied.
3.4
Gaussian Alignment Module
In MIL-based methods under weakly supervised VMR, the general
paradigm is to learn proper deep representation 𝑓𝑣(𝑉) ∈R𝑑model
and 𝑓𝑞(𝑄) ∈R𝑑model that corresponding pairs align closer to each
other via contrastive learning. We extend this idea of video-level

MIL and propose a Gaussian Alignment Module that transforms the
problem to a finer-grained clip-level MIL to train the preceding
Cross-modal Representation Module. Our motivation is that the exis-
tence of glance 𝑔makes frames in the video in-equally important
in terms of the relevance to the query. For a frame 𝑣𝑖, the relevance
is higher when its temporal distance to 𝑔is closer: consider a long
video including scene change, the frames that are too far away
from 𝑔might contain totally irrelevant semantics. Mathematically,
Gaussian distribution has the characteristic that the highest proba-
bility value at the mean point and gradually reduces the probability
to both sides, which aligns consistently to our motivation. Thus,
we use Gaussian distribution to model this relevance. As illustrated
in Figure 3, all video frames are assigned with Gaussian-distributed
weights where the peak position of Gaussian is the glance 𝑔. To
get the weight of the 𝑖-th frame, we scale the index 𝑖∈{1, 2, ..., 𝐿𝑣}
into the domain [−1, 1] by linear transformation
𝑓(𝑖) = (𝑖−1) ·
2
𝐿𝑣−1 −1,
(5)
and sample the Gaussian values via the probability density function
𝐺(𝑖) = norm(
1
√
2𝜋𝜎
exp(−(𝑓(𝑖) −𝑓(𝑔))2
2𝜎2
)),
(6)
where 𝜎is a hyperparameter, and the normalization scales 𝐺(𝑖)
where 𝑖∈[−1, 1] into range [0, 1].
After different weights are assigned across the video frames,
we are able to get video clips with different weights as training
examples. A sliding window of size 𝐿𝑐with stride 𝑠is applied on
the video to get clips. Each clip is then max pooled along the frame
dimension to generate the clip-level feature in the joint embedding
space R𝑑model. To this end, the 𝑖-th clip feature c𝑖is given by
c𝑖= max_pool([v(𝑖−1) ·𝑠+1, ..., v(𝑖−1) ·𝑠+𝐿𝑐]) ∈R𝑑model.
(7)
And each clip is assigned with a clip-level weight 𝑤𝑖sampled at
the middle point of the clip, given by
𝑤𝑖= 𝐺((𝑖−1) · 𝑠+ 𝐿𝑐
2 ).
(8)
Similarly, for the text modality, sentence-level feature s is ob-
tained by max pooling its word-level features, given by
s = max_pool([q1, ..., q𝐿𝑞]) ∈R𝑑model.
(9)
GLS-NCE Loss. In weakly supervised VMR, standard NCE loss
on video level can be directly applied to train the video and query
encoders 𝑓𝑣(·) and 𝑓𝑞(·) by contrasting (𝑉,𝑄)pos against (𝑉,𝑄)neg
in one batch. For one video 𝑉in a batch of 𝐵video query pairs,
there is only one positive matching query 𝑄𝑝, and the rest 𝐵−1
queries are negative queries 𝑄𝑛. Therefore, the standard video-level
NCE loss is given by
LVideo-NCE = −log(
𝑒𝑓𝑣(𝑉)⊤𝑓𝑞(𝑄𝑝)
𝑒𝑓𝑣(𝑉)⊤𝑓𝑞(𝑄𝑝) +
𝐵−1
Í
𝑖=1
𝑒𝑓𝑣(𝑉)⊤𝑓𝑞(𝑄𝑛
𝑖)
).
(10)
·
·
·
·
·
·
Video Frame
Anchor Frame
Sliding Window
Sliding window based inference
Our proposed QAGI
Figure 4: Comparison between sliding window based infer-
ence and our proposed Query Attention Guided Inference.
However, in our method, the proposed GLS-NCE loss is built on
clip level. Each video is substituted by 𝑁clips as in Equation 7. On
this basis, for a video in the same batch of size 𝐵, clip-level NCE
loss is extended to
LClip-NCE = −log(
𝑁
Í
𝑖=1
𝑒c⊤
𝑖𝑓𝑞(𝑄𝑝)
𝑁
Í
𝑖=1
𝑒c⊤
𝑖𝑓𝑞(𝑄𝑝) +
𝑁
Í
𝑖=1
𝐵−1
Í
𝑗=1
𝑒c⊤
𝑖𝑓𝑞(𝑄𝑛
𝑗)
).
(11)
Additionally, the clips also differ in weights given by Equation 8.
To accommodate this, we implement clip-level NCE in the form of
cross-entropy following MoCo [15], and enable the weighting via
label smoothing. In this GLS-NCE loss, the Gaussian weight 𝑤𝑖of
a clip c𝑖is assigned as the label smoothing amount, i.e., instead of
using a one-hot label across the 𝐵queries in the batch, we assign
𝑤𝑖to the label of the positive query, and smooth the rest 𝐵−1
negative labels to 1−𝑤𝑖
𝐵−1 . In summary, for a clip c𝑖with weight 𝑤𝑖,
its GLS-NCE loss is given by
LGLS-NCE = 𝑤𝑖· log(c⊤
𝑖𝑄𝑝) +
𝐵−1
∑︁
𝑗=1
1 −𝑤𝑖
𝐵−1 log(c⊤
𝑖𝑄𝑛
𝑗).
(12)
QAG-KL Loss. To further smooth the learning and to align with
the inference strategy to be explained in Section 3.5, we leverage
the nature of attention mechanism [41] in our encoding module
and propose the QAG-KL loss. Specifically, we use a KL divergence
between the attention distribution of the Query to Video Cross-
modal Encoder (Equation 3) and the Gaussian guidance (Equation
6), to pull the attention distribution across all video frames closer
to the Gaussian guidance. Since the query 𝑄contains 𝐿𝑞words, we
treat them equally and use the mean of their attention distributions
as the sentence-level attention distribution. For the 𝑖-th frame in
the video, the QAG-KL loss is given by
LQAG-KL = 𝐺(𝑖)(log𝐺(𝑖) −a𝑖)
(13)
where a =
𝐿𝑞
Í
𝑗=1
softmax( q𝑗𝐾(v)𝑇
𝑑model/ℎ)
𝐿𝑞
∈R𝐿𝑣.
(14)

The complete loss function of a batch is the combination of the
GLS-NCE loss across all clips in the batch and the QAG-KL loss
across all frames of all videos in the batch, given by
L =
∑︁
c
LGLS-NCE +
∑︁
𝑣
∑︁
𝐿𝑣
LQAG-KL.
(15)
3.5
Query Attention Guided Inference
Due to the lack of 𝑠𝑡and 𝑒𝑑annotations, weakly supervised VMR
methods often compromise to designing two independent pipelines
for training and inference. Under weakly supervised VMR, the most
common inference strategy is to select the best proposal from a se-
ries of proposals generated by methods like sliding window. Despite
that it is still not possible to design a unified pipeline that handles
training and inference consistently under glance annotation, we
propose to use a Query Attention Guided Inference (QAGI) to best
align the inference process to our aforementioned training strategy.
As illustrated in Figure 4, given a video 𝑉and query 𝑄, we first
extract the features v ∈R𝐿𝑣×𝑑model and s ∈R𝑑model via the trained
cross-modal representation module as described in previous sec-
tions. After that, we select an anchor point index 𝑎𝑝∈{1, 2..., 𝐿𝑣}
with the guidance of the query to video attention distribution.
Specifically, the frame where the attention value reaches its maxi-
mum is chosen as the anchor frame, given by
𝑎𝑝= arg max
𝑖
a𝑖.
(16)
A series of proposals are then generated around 𝑎𝑝, i.e., we first
apply a naive sliding window on the 𝐿𝑣frames to generate a pro-
posals pool {𝑝𝑖:𝑗}, then prune out all proposals that does not satisfy
𝑖≤𝑎𝑝≤𝑗. On this basis, the proposal that maximizes the similarity
score to the query is select as our final output, given by
arg max
𝑖,𝑗
max_pool([v𝑖:𝑗])⊤s.
(17)
4
EXPERIMENTS
To validate our proposed glance annotation and the method ViGA,
extensive experiments are conducted on three publicly available
datasets. We also perform ablation studies on different components
in ViGA to investigate their influence in details.
4.1
Datasets
We re-annotate the following datasets to fit in our proposed glance
annotation. Specifically, we augment each example with a uniformly
sampled timestamp 𝑔in range [𝑠𝑡,𝑒𝑑].
ActivityNet Captions. Krishna et al. [20] annotated the Activ-
ityNet v1.3 dataset [3] which was originally designed for video
captioning, and released the ActivityNet Captions dataset for VMR.
It contains 19,994 YouTube videos from diverse domains. Follow-
ing previous studies [49, 50], we use the pre-defined split val_1
as validation set and test on val_2. As a result, 37,421, 17,505, and
17,031 annotations are used for training, validating, and testing,
respectively.
Variants
R@IoU=
mIoU
0.3
0.5
0.7
Video-NCE
35.58
18.30
8.54
25.34
Clip-NCE
16.72
6.25
2.02
14.93
GLS-NCE
59.61
35.79
16.96
40.12
Table 1: Ablation comparison among training with different
NCE loss functions on ActivityNet Captions dataset.
Variants
R@IoU=
mIoU
0.3
0.5
0.7
w/o QAG-KL
54.74
34.26
16.68
37.96
w/ QAG-KL
59.61
35.79
16.96
40.12
Table 2: Ablation comparison between model trained with
and without QAG-KL loss on ActivityNet Captions dataset.
Variants
R@IoU=
mIoU
0.3
0.5
0.7
Sliding Window
58.13
31.23
13.62
38.24
Query Attention Guided Inference
59.61
35.79
16.96
40.12
Table 3: Ablation comparison of inference with query atten-
tion guided inference / naive sliding window on ActivityNet
Captions dataset.
Charades-STA. Gao et al. [12] annotated the Charades dataset
[35] using a semi-automatic approach and formed the Charades-
STA dataset. It contains 9,848 videos of daily indoors activities.
We follow the standard split of 12,408 and 3,720 annotations for
training and testing defined by the annotator.
TACoS. Regneri et al. [33] annotated the MPII Cooking Compos-
ite Activities dataset [34] which was originally designed for activity
recognition, and formed the TACoS dataset. It contains 127 videos
of cooking. We follow the standard split provided by [12], and 9790,
4436, and 4001 annotations are included in training, validation and
test set, respectively.
4.2
Evaluation Metric
We evaluate our method using 1) recall of threshold bounded tem-
poral intersection over union (R@IoU), which measures the per-
centage of correctly retrieved predictions where only the temporal
IoU between the prediction and the ground truth greater than a
certain threshold is accepted, and 2) mean averaged IoU (mIoU)
over all predictions.
4.3
Implementation Details
We fix the 3D CNN modules for extracting visual features for a
fair comparison. For all the three datasets, we use C3D as feature
extractor. Since Charades-STA lacks a unified standard of feature
extractor in previous studies, additional experiments using I3D and
VGG features are also conducted for the completeness of compari-
son. For the word embedding, we adopt 840B GloVe for building a
most complete vocabulary. To increase the capacity of our encoders,
we stack two layers of our query, video and cross-modal encoders.

0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
sigma
10
20
30
40
50
60
Recall@1
IoU=0.3
IoU=0.5
IoU=0.7
mIoU
36
37
38
39
40
mIoU
Figure 5: Influence of the hyperparameter 𝜎of the Gaussian
distribution in Equation 6 on ActivityNet Captions dataset.
The model dimension 𝑑model is set to 512, and the number of atten-
tion heads ℎis set to 8 globally. Our model is trained with AdamW
[26] with a learning rate of 0.0001 half decaying on plateau. We
clip the gradient norm to 1.0 during training. The batch size and
𝜎factor of the three datasets are empirically set to (256, 0.4), (256,
0.3) and (128, 1.0), respectively. All experiments are conducted on a
Nvidia Tesla V100 GPU with 32GB memory.
4.4
Ablation Studies
To evaluate the effectiveness of different components in our pro-
posed ViGA, we conduct extensive ablation experiments on the
ActivityNet Captions dataset.
Effects of GLS-NCE. In order to verify the effectiveness of our
proposed GLS-NCE loss (Equation 12), we compare it with the
aforementioned variants Video-NCE loss (Equation 10) and Clip-
NCE loss (Equation 11). The Video-NCE treats the video as a whole
and maximizes the similarity between it and the text query. The
Clip-NCE cuts a video into many clips, which increases the number
of examples in the batch. However, as the distance between the
clip and glance increases, its relevance to the query becomes lower.
Therefore, our GLS-NCE assigns different weights to different clips
according to their temporal distances to the glance. The results
are listed in Table 1. The performance of GLS-NCE is significantly
ahead of others, thus showing its effectiveness. Besides, it is worth-
while to note that scores of Clip-NCE are almost half of Video-NCE,
indicating that simply increasing the number of samples through
clip segmentation is not beneficial, but sharply harms the perfor-
mance instead. Comparing the three groups of experiments, we
conclude that the performance improvement of GLS-NCE is not
brought by increasing the number of examples by slicing a video
into clips, while the enhancement from Gaussian label smoothing
makes the main contribution.
Effects of QAG-KL. The QAG-KL loss (Equation 14) encourages
the model to pay more attention to the glance frame and its near
neighbors in the training stage. To validate the its effectiveness, we
conduct the ablation study of simply removing the QAG-KL loss.
From the results in Table 2, we have the following observations.
First, QAG-KL improves the moment retrieval performance on all
evaluation metrics. This shows that in the training stage, QAG-KL
can indeed make use of the prior information of glance annotation
and help the model with better cross-modal alignment learning.
Second, the performance with QAG-KL increases more significantly
when the IoU threshold is 0.3 than other thresholds, reaching around
5%. We consider this gap is due to the fact that glance is a relatively
weak prior information, so it performs better when the requirement
of retrieval precision (reflected by the IoU) is not strict.
Sliding Window vs. Query Attention Guided Inference. To
verify the effectiveness of our proposed QAGI, we evaluate the
same trained model under different testing strategy, i.e., naive slid-
ing window vs. QAGI. The results in Table 3 show that QAGI has
advantages over the traditional sliding window based evaluation on
all metrics. QAGI uses the attention matrix learned in the training
stage to obtain the anchor frame for generating proposals in the
test stage, which can filter out irrelevant proposals to a great extent,
especially those with short durations. It is worthwhile to note that
the improvement is more obvious under the metric with larger IoU
threshold, as the performance raises by 4.5% and 3.3% respectively
at IoU threshold of 0.5 and 0.7. This suggests that using the anchor
is beneficial especially when the retrieval precision requirement is
relatively high.
Effects of the Gaussian distribution parameter 𝝈. In this
ablation study, we focus on the hyperparameter 𝜎in Equation 6.
Theoretically, 𝜎describes the dispersion degree of a Gaussian distri-
bution: the larger the 𝜎, the flatter the curve. In the context of our
Gaussian Alignment Module, the value of 𝜎controls to what extent
that the weight at the glance frame which is always 1.0 disperses to
other frames, hence affecting the overall positiveness of all the clips
in the video. Consider an extreme example, when 𝜎takes a very
large value, all frames in the video are assigned with weights close
to 1.0. This means that we take all clips almost equally positive,
which reduces the learning to be approximately equivalent to the
video-level MIL under weak supervision. Therefore, choosing an ap-
propriate 𝜎is important. As reported in Figure 5, as 𝜎increases, the
performance of the four metrics first increases and then decreases.
Specifically, when 𝜎is set to 1.2, i.e., we over-assign positiveness
to the clips, the performance of the four metrics decreases sharply
(e.g., mIoU decreases from 40 to 36). On the other hand, when 𝜎is
very small, i.e., we only take a very narrow range of video clips as
important positive examples, the performance decreases because of
losing some clips that are in fact informative positive examples (e.g.,
when 𝜎is set to 0.05, mIoU decreases by 2%). On the ActivityNet
Captions dataset, the performance achieves its best when 𝜎is set to
a medium value 0.4. This observation coincides with our theoretical
analysis.
4.5
Comparison with State of the Art
We compare the proposed ViGA with both fully and weakly super-
vised methods, which are introduced as follows.
Compared Methods. As shown in Table 4, we divide the com-
pared methods into three sections according to the supervision

Supervision
Method
Charades-STA
ActivityNet Captions
TACoS
R@0.3
R@0.5
R@0.7
mIoU
R@0.3
R@0.5
R@0.7
mIoU
R@0.3
R@0.5
R@0.7
mIoU
Full
Supervision
CTRL [12]
-
23.63
8.89
-
-
-
-
-
18.32
13.3
-
-
QSPN [45]
54.7
35.6
15.8
-
45.3
27.7
13.6
-
-
-
-
-
2D-TAN [49]
-
39.7
23.31
-
59.45
44.51
26.54
-
37.29
25.32
-
-
LGI [29]
72.96
59.46
35.48
51.38
58.52
41.51
23.07
41.13
-
-
-
-
VSLNet [48]
70.46
54.19
35.22
50.02
63.16
43.22
26.16
43.19
29.61
24.27
20.03
24.11
Weak
Supervision
TGA [28]
32.14
19.94
8.84
-
-
-
-
-
-
-
-
-
SCN [23]
42.96
23.58
9.97
-
47.23
29.22
-
-
-
-
-
-
BAR [44]
44.97
27.04
12.23
-
49.03
30.73
-
-
-
-
-
-
VLANet [27]
45.24
31.83
14.17
-
-
-
-
-
-
-
-
-
MARN [37]
48.55
31.94
14.81
-
47.01
29.95
-
-
-
-
-
-
LoGAN [39]
51.67
34.68
14.54
-
-
-
-
-
-
-
-
-
CRM [17]
53.66
34.76
16.37
-
55.26
32.19
-
-
-
-
-
-
Glance
Supervision
2D-TAN [49] †
-
-
-
-
11.26
5.28
2.34
-
13.97
5.50
1.60
-
LGI [29] †
51.94
25.67
7.98
30.83
9.34
4.11
1.31
7.82
-
-
-
-
ViGA (C3D)
56.85
35.11
15.11
36.35
59.61
35.79
16.96
40.12
19.62
8.85
3.22
15.47
ViGA (VGG)
60.22
36.72
17.20
38.62
-
-
-
-
-
-
-
-
ViGA (I3D)
71.21
45.05
20.27
44.57
-
-
-
-
-
-
-
-
Table 4: Performance comparison with the state-of-the-art methods under different supervision settings. “†” denotes our re-
implemented results of fully supervised methods under glance annotations. In order to align with their original design, we
give a relaxed glance condition by shrinking the original annotations to a random 3-seconds duration instead of one instant
timestamp as in our results.
types, including full supervision, weak supervision and glance su-
pervision. When selecting methods from the literature to compare,
we follow the rule of diversely selecting representative methods in
different categories as introduced in Section 2 for the completeness
of the comparison. For two-stage fully supervised methods, CTRL
[12] is sliding window based and QSPN [45] is proposal based. In
the end-to-end fully supervised methods, 2D-TAN [49] belongs
to anchor based, while LGI [29] and VSLNet [48] are anchor free.
For weak supervision, a dominant number of methods adopt MIL
strategy than query reconstruction. Therefore, we select MIL-based
methods like TGA [28], VLANet [27], LoGAN [39], CRM [17] and
one representative reconstruction-based method SCN [23]. Cur-
rently, CRM is the state of the art in weakly supervised VMR.
In addition to these existing studies, we apply glance annotation
to two well-recognized fully supervised methods (i.e., 2D-TAN and
LGI) for a more direct comparison to our proposed ViGA. In order
to align with their original design, we give a relaxed glance condi-
tion by shrinking the original annotations to a random 3-seconds
duration instead of one instant timestamp. Practically, we achieve
this by directly changing the annotations in the data and run their
publicly available source codes.
Observations and Discussions. According to the results in Ta-
ble 4, we can make a number of observations worthy discussing.
1. In terms of all metrics, our proposed approach significantly
exceeds the methods under weak supervision on the Charades-STA
and ActivityNet Captions dataset. We improve the recall by 7%,
11% and 4% on Charades-STA when IoU is 0.3, 0.5 and 0.7, respec-
tively. On ActivityNet Captions, the improvement is 5% and 4%
when IoU is 0.3 and 0.5, respectively. We believe that on one hand,
it shows that the setting of glance annotation is reasonable and
has good potential in performance, and on the other hand, it also
shows that ViGA succeeds in exploiting the information provided
by glance annotation. In addition, in order to make ViGA standard
and concise, we did not use some effective tricks in weak super-
vision methods, such as surrogate proposal selection in VLANet
and temporal semantic consistency in CRM. This may take the
performance of ViGA further, and we leave this as future work.
2. When comparing to some fully supervised methods, we are
surprised to find that when IoU is small (e.g., 0.3), our method almost
reaches a same performance level. For example, on Charades-STA,
our R@1 IoU=0.3 is 71.21%, 1.6% lower than LGI and 0.8% higher
than VSLNet. On ActivityNet Captions, the recall is 59.61%, 1.9%
higher than LGI and 3.6% lower than VSLNet. This suggests that
under the scenario of coarse-grained retrieval requirements, glance
annotation might be more advantageous than full annotation with
acceptable performance yet significantly lower cost. However, there
is still a lot of space for improvement when a high retrieval precision
is required. For example, when the R@1 IoU=0.7, the performance
gap between ViGA and LGI on Charades-STA reaches 15.21%.
3. For the previously mentioned fully supervised method re-
implemented under relaxed glance annotation, we have the follow-
ing findings. First, although we relax the setting of glance to 3 sec-
onds, our approach shows superior performance in all three datasets.
Second, we observe that the performance of re-implemented meth-
ods on ActivityNet Captions is not sufficiently satisfying. Therefore,
it might be inadvisable to transplant fully supervised methods to
glance annotation setting by directly changing the annotation to a
instant moment or a short time duration, especially for the dataset
like ActivityNet Captions, which generally has a long video dura-
tion and a wide range of moment lengths.
4. As can be seen from Table 4, weakly supervised methods are
often not tested on TACoS dataset because the videos in TACoS are

Query: A small child is seen standing on a base with an older man pointing.
Ground Truth
Prediction
Q2V
Attention
Video (1)
0.0s - 25.5s
0.0s - 24.7s
Attn_max=11.2s
Video duration=124.2s
Query: They put soap on their hands and scrub them together.
Ground Truth
Prediction
Q2V
Attention
Video (2)
7.6s - 75.0s
18.9s - 81.8s
Glance=1.2s, Attn_max=11.2s
Video duration=89.8s
Attn_max=59.3s
Query: He gets down on the ground and flips around.
Ground Truth
Prediction
Q2V
Attention
Video (3)
43.1s - 55.5s
9.3s - 66.6s
Glance=1.2s, Attn_max=11.2s
Video duration=73.1s
Attn_max=24.0s
Figure 6: Some visualized examples from the test split of ActivityNet Captions. The first two examples are successful and the
third is a failing case.
very long and the moments to be retrieved are too short, i.e., the
requirement of retrieval precision is very high. It might be hard for
existing weakly supervised methods to deal with this situation. Our
proposed ViGA shows positive in such case with a similar result to
early fully supervised methods, such as CTRL.
4.6
Qualitative Analysis
Figure 6 shows some qualitative examples from the test split of
ActivityNet Captions dataset, in which the green bar is the ground
truth temporal boundary of the language query and the blue bar
represents the predicted boundary of ViGA. We also visualize the
query-to-video attention (pink curve under the video flow) to il-
lustrate our proposed QAG-KL loss and query attention guided
inference. Video (1) and Video (2) are successfully retrieved sam-
ples with high IoU. They show the effectiveness of our method
from two aspects. For video (1), the video duration is very long
(up to 124.2 seconds) and the moment to be retrieved is relatively
short (25.5 seconds), which reveals that our proposed approach
based on glance annotation can locate precisely when the video
semantics is complex. As can be seen from the plot, this is benefited
from a reasonable query-to-video attention distribution which is
precisely positioned in the correct moment interval. On one hand, it
enhances the cross-modal representation learning, and on the other
hand, it provides a good anchor frame for inference. For video (2),
we observe that ViGA successfully retrieves this long moment of
nearly one minute. Given that we might be able to have good results
of retrieving long segments under single frame glance annotation,
it is reasonable to conjecture that the length of full annotation could
have been reduced, even not to the extreme of just one single frame.
Therefore, our qualitative results are in favor of the great potential
of glance annotation. Inevitably, there are also failing cases. For
example, in Video (3), the language query corresponds to a short
clip of the man gets down on the ground and flips around, but our
model recalls a long range segment containing the man, includ-
ing a large part of the man standing, showing a lack of sufficient
understanding of the fine-grained textual semantics. We consider

that this is the hard part in the task of retrieving video moments
with free-form text query. There is not sufficiently large amount
of training data for learning fine-grained semantics because the
free-form text query has great diversity. The model can be easily
confused about some fine-grained actions, such as “get down on
the ground and flip around” here.
5
LIMITATIONS
Due to our limited resource, we are only able to re-annotate the
datasets in an automatic way by doing random sample in the time in-
terval of original annotation instead of manually annotating them.
Although we achieve good results in the previous experiments,
there are some inevitable problems in this simple re-annotation
strategy. For example, some queries might contain multiple seman-
tics, which are not possible to be captured by only one glance. Also,
in some rare cases, meaningless frames that would pollute the train-
ing data such as camera scene changes might be sampled as the
glance, which could have been filtered out manually. We hope a
manually annotated dataset in glance annotation could be collected
in the future to support follow-up research in this direction.
6
CONCLUSION
In this paper, we study the problem of VMR. After analysing the
advantages and limitations of the two existing VMR paradigms fully
supervised VMR and weakly supervised VMR, we find that weakly
supervised VMR can be augmented with trivial cost, and propose a
new data annotation paradigm named as glance annotation. Under
glance annotation, we propose ViGA, a novel clip-level contrastive
learning framework, as a pioneer method to solve this problem.
Extensive experiments are conducted on three publicly available
datasets ActivityNet Captions, Charades-STA and TACoS, and ViGA
outperforms existing weakly supervised methods by a large margin.
Therefore, we conclude that glance annotation is a promising new
data annotation paradigm for VMR, and ViGA is a feasible method
for glance annotated VMR. Our results support further research
and applications of glance annotation in real-life problems.
ACKNOWLEDGEMENT
This research is conducted within the first two authors’ intern-
ship in bilibili. We are grateful to bilibili AI for the support and
inspiration.
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D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with
Glance Annotation
Hanjun Li1, Xiujun Shu1, Sunan He2, Ruizhi Qiao1, Wei Wen1, Taian Guo1, Bei Gan1, Xing Sun1*
1Youtu Lab, Tencent
2Hong Kong University of Science and Technology
{hanjunli, xiujunshu, ruizhiqiao, jawnrwen, taianguo, stylegan, winfredsun}@tencent.com
sunan.he@connect.ust.hk
Abstract
Temporal sentence grounding (TSG) aims to locate a
specific moment from an untrimmed video with a given nat-
ural language query. Recently, weakly supervised methods
still have a large performance gap compared to fully super-
vised ones, while the latter requires laborious timestamp
annotations. In this study, we aim to reduce the annotation
cost yet keep competitive performance for TSG task com-
pared to fully supervised ones. To achieve this goal, we
investigate a recently proposed glance-supervised temporal
sentence grounding task, which requires only single frame
annotation (referred to as glance annotation) for each
query. Under this setup, we propose a Dynamic Gaussian
prior based Grounding framework with Glance annotation
(D3G), which consists of a Semantic Alignment Group Con-
trastive Learning module (SA-GCL) and a Dynamic Gaus-
sian prior Adjustment module (DGA). Specifically, SA-GCL
samples reliable positive moments from a 2D temporal map
via jointly leveraging Gaussian prior and semantic consis-
tency, which contributes to aligning the positive sentence-
moment pairs in the joint embedding space. Moreover, to al-
leviate the annotation bias resulting from glance annotation
and model complex queries consisting of multiple events,
we propose the DGA module, which adjusts the distribu-
tion dynamically to approximate the ground truth of tar-
get moments. Extensive experiments on three challenging
benchmarks verify the effectiveness of the proposed D3G.
It outperforms the state-of-the-art weakly supervised meth-
ods by a large margin and narrows the performance gap
compared to fully supervised methods. Code is available at
https://github.com/solicucu/D3G.
1. Introduction
Temporal sentence grounding is a fundamental prob-
lem in computer vision and receives an increasing atten-
*Corresponding author
Query: person pours some water into a glass.
5.9 s (8.0 s) 15.4 s
𝑔
positive pair
negative pair
slide window
ViGA
Duration: 21.96 s
DGA
positive pair
negative pair
D3G
2d map
5.9 s (8.0 s) 15.4 s
𝑔
Figure 1. Illustration of glance annotation g (red dashed line) and
simple comparison between ViGA and D3G. The red rectangle
indicates the boundary of target moment.
tion in recent years.
Given the query sentence and an
untrimmed video, the goal of TSG is to localize the start and
end timestamps of specific moment that semantically corre-
sponds to the query. In recent years, full supervised tem-
poral sentence grounding (FS-TSG) has achieved tremen-
dous achievements [9, 1, 41, 43, 34, 29, 33, 42]. However,
obtaining accurate timestamps for each sentence is labor-
intensive and subjective, which prevents it from scaling to
large-scale video-sentence pairs and practical applications.
Weakly supervised temporal sentence grounding (WS-
TSG), which requires only the video and query pairs, re-
ceives an increasing attention recently. Although great ad-
vances [19, 32, 12, 45, 43, 44] have been achieved in recent
years, there still remains a huge performance gap between
WS-TSG and FS-TSG. WS-TSG suffers from severe local-
ization issues due to the large discrepancy between video-
level annotations and clip-level task.
Recently, Cui et al. [6] propose a new annotating
paradigm called glance annotation for TSG, requiring the
timestamp of only random single frame within the tempo-
ral boundary of the target moment. It is noted that such
annotation only increases trivial annotating cost compared
to WS-TSG. Figure 1 illustrates the details of glance an-
notation. With glance annotation, Cui et al. propose the
arXiv:2308.04197v1 [cs.CV] 8 Aug 2023

ViGA based on contrastive learning. ViGA first cuts the in-
put video into clips of fixed length, which are assigned with
Gaussian weights generated according to the glance anno-
tation, and contrasts clips with queries. There are two ob-
vious disadvantages in this way. First, moments of interest
usually have various durations. Therefore, these clips can-
not cover a wide range of target moments, which inevitably
aligns the sentence with incomplete moment and obtains
sub-optimal performance. Second, ViGA utilizes a fixed
scale Gaussian distribution centered at the glance frame to
describe the span of each annotated moment. However, the
glance annotations are not guaranteed at the center of target
moments, which results in annotation bias as shown in Fig-
ure 2. Besides, since some complex query sentences consist
of multiple events, a single Gaussian distribution is hard to
cover all events at the same time as shown in Figure 3. To
address the aforementioned defects and fully unleash the
potential of Gaussian prior knowledge with the low-cost
glance annotation, we propose a Dynamic Gaussian prior
based Grounding framework with Glance annotation (D3G)
as shown in Figure 4.
We first generate a wide range of candidate moments fol-
lowing 2D-TAN [43]. Afterwards, we propose a Semantic
Alignment Group Contrastive Learning module (SA-GCL)
to align the positive sentence-moment pairs in the joint em-
bedding space. Specifically, for each query sentence, we
sample a group of positive moments according to calibrated
Gausssian prior and minimize the distances between these
moments and the query sentence. In this way, it tends to
gradually mine the moments which have increasing over-
lap with the ground truth. Moreover, we propose a Dy-
namic Gaussian prior Adjustment module (DGA), which
further alleviates annotation bias and approximates the span
of complex moments consisting of multiple events. Specif-
ically, we adopt multiple Gaussian distributions to describe
the weight distributions of moments. Therefore, the weight
distributions for various moments can be flexibly adjusted
and gradually approach to the ground truth. Our contribu-
tions are summarized as follows:
• We propose a Dynamic Gaussian prior based Ground-
ing framework with Glance annotation (D3G), which
facilitates the development of temporal sentence
grounding with lower annotated cost.
• We propose a Semantic Alignment Group Contrastive
Learning module to align the features of the positive
sentence-moment pairs and a Dynamic Gaussian prior
Adjustment module to ease the annotation bias and
model the distributions of complex moments.
• Extensive experiments demonstrate that D3G obtains
consistent and significant gains compared to method
under the same annotating paradigm and outperforms
weakly supervised methods by a large margin.
𝑤!
𝑤"
𝑤#
𝑤!
,
𝑤"
,
𝑤#
,
target moment
target moment
𝑔
𝑔
Figure 2. Illustration of annotation bias and Gaussian prior after
dynamic adjustment. Top: the target moment is assigned with a
low weight wm due to the bias of glance annotation according to
ViGA, which we call annotation bias. Bottom: a reasonable Gaus-
sian distribution is obtained via DGA described in Section 3.3.
2. Related Work
Full Supervised Temporal Sentence Grounding. The FS-
TSG methods can be categorized into two groups. Two-
stage methods [1, 9, 11, 13, 14, 35] first propose candi-
date segments in a video through sliding window or pro-
posal generation. A cross-modal matching network is then
employed to find the best matching clip. However, these
propose-and-match paradigms are time-consuming due to
the numerous candidates. To reduce the redundant com-
putation, some researchers proposed single-stage meth-
ods [2, 3, 40, 41, 30, 20, 43, 21, 39, 37]. 2D-TAN [43]
constructs 2D feature map to model the temporal relations
of video segment. Recently, Wang et al. [33] propose a Mu-
tual Matching Network based on 2D-TAN, and further im-
prove the performance via exploiting both intra- and inter-
video negative samples. Although fully supervised meth-
ods achieve satisfying performance, they are highly depen-
dent on accurate timestamp annotations. It is highly time-
consuming and laborious to obtain these annotations for
large-scale video-sentence pairs.
Weakly Supervised Temporal Sentence Grounding.
Specifically,
WS-TSG methods can be grouped into
reconstruction-based methods [8, 18, 26, 4] and multi-
instance learning (MIL) methods [19, 10, 5, 38, 12, 27].
SCN [18] employs a semantic completion network to re-
cover the masked words in the query sentence with the gen-
erated proposals, which provides feedback for facilitating
final predictions. To further exploit the negative samples in
MIL-based methods, CNM [44] and CPL [45] propose to
generate proposals with Gaussian functions and introduce
intra-video contrastive learning. WS-TSG methods indeed
advance with low annotation cost, however, there still re-
mains a large performance gap compared to FS-TSG meth-
ods due to the discrepancy between video-level annotations
and clip-level task.
Glance Supervised Temporal Sentence Grounding. Re-

…
…
A person walks on the roof of a climbing equipment
then he goes down the wall
and reach the floor and walk forward.
g
Figure 3. Illustration of complex query consists of multiple events.
Note that g indicates the position of glance annotation. The ac-
cording Gaussian distribution (red curves) is hard to cover the
whole target moments. We utilize DGA module to mine multiple
latent Gaussian distributions (dashed line) to model such query.
cently, ViGA [6] proposes glance supervised TSG (GS-
TSG) task with a new annotating paradigm.
ViGA uti-
lizes a Gaussian function to model the relevance of differ-
ent clips with target moment and contrasts the clips with the
queries. Though ViGA achieves promising performance, it
still suffers from two limitations as mentioned in Introduc-
tion. Concurrently, Xu et al. [36] propose the similar task
called PS-VTG, and generate pseudo segment-level labels
based on language activation sequences. To better explore
the Gaussian prior for TSG task with glance annotation, we
propose a simple yet effective D3G, which achieves com-
petitive performance compared with both WS-TSG and FS-
TSG methods. Concurrent with our work, Ju et al. [15]
propose a robust partial-full union framework (PFU) and
achieve excellent performance with glance annotation or
short-clip labels.
3. Proposed Method
3.1. Overview
Given an untrimmed video V and query sentence S, the
temporal sentence grounding task aims to determine the
start timestamp ts and end timestamp te, where the mo-
ment Vts:te best semantically corresponds to the query. As
for FS-TSG, the exact timestamps (ts,te) of corresponding
moment is provided given a query description. In contrast,
Cui et al. [6] propose a new low-cost annotating paradigm
called glance annotation, which requires only single times-
tamp g, satisfying g ∈[ts, te]. Following the setting of [6],
we propose a Dynamic Gaussian prior based Grounding
framework with Glance annotation (D3G) to fully unleash
the potential of glance annotations.
Our D3G adopts the network architecture similar to
[43, 33].
Given an untrimmed video, we firstly encode
the video into feature vectors with pre-trained 2D or 3D
convolutional network [25, 28] and segment the video fea-
tures into N video clips. Specifically, we apply average
pooling to each clip to obtain clip-level features V
=
{f v
1 , f v
2 , ..., f v
N} ∈RN×Dv. These clip features are then
passed through an FC layer to reduce their dimension, de-
noted as F 1d ∈RN×dv. Afterwards, we encode them as
2D temporal feature map ˆ
F ∈RN×N×dv following 2D-
TAN [43] with the max pooling. As for language encoder,
we choose DistilBERT [23] to obtain sentence-level feature
ˆ
f s ∈Rds following [33]. Finally, to estimate the matching
scores of candidate moments and the query, we utilize a lin-
ear projection layer to project the textual and visual features
into same dimension d, respectively. The final representa-
tion of sentence is f s ∈Rd and the features of all moments
are F ∈RN×N×d. The final matching scores are given by
the cosine similarity between f s and elements of F.
3.2. Semantic Alignment Group Contrastive Learn-
ing
In this section, we aim to mine the moment which most
semantically corresponds to the query and maximize the
similarity between them.
To achieve this goal, we have
two crucial steps. First, we generate abundant candidate
moments following 2D-TAN and assign them with reli-
able Gaussian prior weights generated with the guidance of
glance annotation. Second, we propose a semantic align-
ment group contrastive learning to align a group of positive
moments with corresponding query sentence.
To be specific, given the encoded video features F 1d ∈
RN×dv and glance annotation g, we also utilize a Gaussian
function parameterized with (µ, σ) to model the relations
between frames and target moment, where the µ is deter-
mined by the glance g. We first scale the sequence indices
I ∈{1, 2, ..., N} into domain [−1, 1] by a linear transfor-
mation as follows:
h(i) = 2· i −1
N −1 −1.
(1)
Given the index i, we can obtain corresponding Gaussian
weight via Eq. (2).
G(i, µ, σ) = Norm(
1
√
2πσ exp(−(h(i) −h(µ))2
2σ2
)), (2)
where µ ∈I and σ is a hyperparameter, and Norm(·) is a
function used to scale values into range [0, 1].
Different from ViGA [6], we utilize the characteristic
of 2D-TAN to generate a wide range of candidate mo-
ments with various durations.
Given the video features
F 1d ∈RN×dv, we encode them into 2D feature map
F ∈RN×N×d as shown in Figure 4, where Fij denotes
the feature of moment that starts at position i and ends at
position j. Note that the moment is valid only when i ≤j.
We then propose a triplet-sample strategy to generate more
reasonable weights for candidate moments instead of only
sampling the weight at middle point as in [6]. Specifically,

𝑤!"
update
pull
push
intra-video positive
SA-GCL
DGA
Video
feature
extractor
max pooling
& conv & proj
Query: person pours
some water into a glass.
DistillBERT
intra-video negative
inter-video negative
text feature
𝐹∈𝑅#×#×%
𝑓& ∈𝑅%
𝐹'% ∈𝑅#×%!
𝑓&
𝐹(
𝐹)
proj
𝑤!"
𝑠!"
𝑔
Figure 4. The overview of proposed D3G, which consists of Semantic Alignment Group Contrastive Learning (SA-GCL) and Dynamic
Gaussain prior Adjustment (DGA). Note that g indicates the position of glance annotation and the grids with dashed line in F are invalid
candidate moments. “proj” denotes the linear projection layer. “intra/inter” indicate the positive or negative moments sampled from
same/different videos.
for each moment with start position i and end position j, we
compute its Gaussian prior weight as follows:
wij = 1
3· (G(i, g, σ)+G(j, g, σ)+G(⌊i + j
2
⌋, g, σ)), (3)
where g is glance annotation for current target moment. In
this way, those moments containing target moment but hav-
ing longer durations will be penalized with lower weights.
To remedy the annotation bias, we additionally intro-
duce semantic consistency prior to calibrate the Gaussian
prior weight wij for each candidate moment. Given the
query features f s ∈Rd and the features F ∈RN×N×d of
candidate moments, we compute their semantic consistency
scores via Eq. (4).
sij =
f s · Fij
∥f s ∥∥Fij ∥,
(4)
where ∥· ∥is l2-norm. Afterwards, we rectify the Gaussian
weight wij with semantic consistency score sij via multi-
plication to obtain new prior weight pij = wij· sij.
The objective of Temporal Sentence Grounding is to
learn a cross-modal embedding space, where the query sen-
tence feature should be well aligned with the feature of
corresponding moment and far way from those of irrele-
vant video moments. Motivated by [31, 17], we propose
a Semantic Alignment Group Contrastive Learning module
(SA-GCL) to gradually mine candidate moments most se-
mantically aligned with given query sentence. To be spe-
cific, we first sample top-k candidate moments from F as
positive keys for query f s according to the new prior pij,
denoted as F p = {Fij|1 ≤i ≤j ≤N} ∈Rk×d. Simulta-
neously, we sample Gaussian weights of corresponding mo-
ments denoted as W p = {wij|1 ≤i ≤j ≤N} ∈Rk. We
then gather other candidate moments which do not contain
the glance g from intra-video and all candidate moments
from other videos within same batch as negative keys, de-
noted as F n = {Fij|1 ≤i ≤j ≤N} ∈RNn×d, where Nn
denotes the number of negative moments. The objective of
SA-GCL can be described as follows:
Lalign = −1
k
k
X
z=0
W p
z log exp(f s · F p
z /τ)
SUM
,
SUM =
k
X
z=0
exp(f s · F p
z /τ) +
Nn
X
z=0
exp(f s · F n
z /τ),
(5)
where τ is the temperature scaling factor. SA-GCL aims to
maximize the similarity between the query f s and a group
of corresponding positive moments F p under the joint em-
bedding space while pushing away negative pairs. Note that

different positive moments are assigned with corresponding
prior weight W p
z . In this way, SA-GCL effectively avoids
being dominated by inaccurate moments with less similar-
ity and tends to mine the candidate moments having large
overlap with the target moment.
3.3. Dynamic Gaussian prior Adjustment
To further ease the annotation bias and characterize com-
plex target moments, we propose a novel Dynamic Gaus-
sian prior Adjustment module (DGA). Specifically, we uti-
lize multiple Gaussian functions with different centers to
model the local distributions of target moment and aggre-
gate them to approximate the distribution of target moment.
Given the video features F 1d ∈RN×dv and annotation
glance g, we compute the relevance of other position i with
position g via Eq. (6).
rgi =
F 1d
g
· F 1d
i
∥F 1d
g
∥∥F 1d
i
∥.
(6)
¯
rgi = (1 −α)¯
rgi + αrgi.
(7)
To make the relevance scores more stable, we update
¯
rgi with momentum factor α as shown in Eq. (7), where
¯
rgi = rgi at first training epoch. According to the relevance
{¯
rgi}, we can mine latent local centers for target moment.
Specifically, we utilize a specific threshold Tr to filter the
candidate positions and obtain a mask Mg ∈{0, 1}N for
glance g as follows:
M i
g =
(
1,
if ¯
rgi ≥Tr
0,
otherwise
(8)
With the mask of latent local centers, we then adjust the
Gaussian prior dynamically via Eq. (9).
ˆ
G(i, g, σ) = 1
C
N
X
z=1
M z
g · ¯
rgi · G(i, z, σ),
(9)
where C is the summation of mask Mg. Afterwards, we
replace the G(i, g, σ) in Eq. (3) with ˆ
G(i, g, σ), and nat-
urally obtain dynamic Gaussian prior weight during train-
ing. Compared to ViGA, our dynamic Gaussian prior is
more flexible and able to adjust the center of Gaussian dis-
tribution adaptively. Therefore, DGA further alleviates the
annotation bias and provides more reliable prior weights.
Besides, multiple Gaussian distributions are well suited for
modeling complex target moments consisting of multiple
events as shown in Figure 3. DGA tends to widen the re-
gion of high Gaussian weight via self-mining neighboring
frames based on the feature of glance g and gradually gen-
erates the Gaussian prior weight well aligned with target
moment. In this way, SA-GCL will be provided with pos-
itive moments of high quality, which eventually promotes
the cross-modal semantic alignment learning and accurate
localization of target moments.
Discussion. To clearly distinguish the differences between
D3G and few similar works, we give some explanations
here. As for MMN, D3G shares the same process of gener-
ating candidate moments following 2D-TAN, which is not
the key contribution of our method.
MMN utilizes nor-
mal one-to-one contrastive learning is no longer suitable
to glance annotation. However, D3G instead adopts a suit-
able sample strategy and corresponding adapted group con-
trastive learning, which is key component to unleash the po-
tential of glance annotations. As for CPL, we also know that
it utilizes multiple Gaussian distribution to describe positive
moments. However, it actually select one most matched
positive moment guided by the loss of masked language
reconstruction for contrastive learning, while D3G utilizes
multiple Gaussian functions to adaptively model complex
queries consisting of multiple events and samples a group
of positive moments for contrastive learning.
4. Experiments
In order to validate the effectiveness of the proposed
D3G, we conduct extensive experiments on three publicly
available datasets: Charades-STA [9], TACoS [9] and Ac-
tivityNet Captions [16].
4.1. Datasets
Charades-STA is built on dataset Charades [24] for tem-
poral sentence grounding.
It contains 12,408 and 3,720
moment-sentence pairs for training and testing.
TACoS consists of 127 videos selected from the MPII
Cooking Composite Activities video corpus [22]. We fol-
low the standard split from [9], which contains 10,146,
4,589 and 4,083 moment-sentence pairs for training, vali-
dation and testing, respectively. We report the evaluation
result on the test set for fair comparison.
ActivityNet Captions is originally designed for video cap-
tioning and recently introduced into temporal sentence
grounding. It contains 37,417, 17,505 and 17,031 moment-
sentence pairs for training, validation and testing, respec-
tively. We report the evaluation result following [43, 33].
Specially, we adopt the glance annotation released by [6]
for training set, where the temporal boundary is replaced
with the timestamp g uniformly sampled within the original
temporal boundary.
4.2. Evaluation Metric and Implementation Details
Evaluation Metric.
Following previous works [9, 43],
we evaluate our model with metric ‘R@n,IoU=m’, which
means the percentage of at least one of the top-n results
having Intersection over Union (IoU) larger than m. Specif-
ically, we report the results with m
∈
{0.5, 0.7} for

Charades-STA, m ∈{0.3, 0.5, 0.7} for TACoS and Activi-
tyNet Captions, and n ∈{1, 5} for all datasets.
Implementation Details. In this work, our main frame-
work is extended from MMN [33] and most of experiment
settings keep the same. For fair comparison, following [33],
we adopt off-the-shelf video features for all datasets (VGG
feature for Charades and C3D feature for TACoS and Ac-
tivityNet Captions). Specifically, the dimension of joint fea-
ture space d is set to 256 and τ is set to 0.1. In SA-GCL, we
set the k as 10, 20 and 20 for Charades, TACoS and Activ-
ityNet Captions, respectively. The σ in Eq. (2) is set to 0.3,
0.2 and 0.6 for Charades, TACoS and ActivityNet Captions.
In DGA, Tr and α is set as 0.9 and 0.7, respectively.
4.3. Comparisons with the State-Of-The-Art
In order to provide comprehensive analysis, we com-
pare the proposed D3G with both fully/weakly/glance su-
pervised methods. As shown in Table 1, Table 2 and Ta-
ble 3, D3G achieves highly competitive results on three
datasets under glance supervision, and achieves compara-
ble performance compared with fully supervised methods.
Note that we highlight the best value for each setting re-
spectively. Based on the experimental results, we can draw
the following conclusions:
(1) Glance annotation provides more potential to achieve
better performance for temporal sentence grounding with
lower annotation cost. Although it is not entirely fair to
directly compare D3G with other weakly supervised meth-
ods due to introducing extra supervision, D3G significantly
exceeds most of weakly supervised methods by a large mar-
gin with trivial increment of annotation cost. Since PS-VTG
and PFU adopt more robust I3D feature, they obviously out-
perform D3G on Charades-STA. However, D3G instead is
superior to PS-VTG on more challenging TACoS with same
features. Besides, weak supervised methods are often not
tested on TACoS, where the videos are very long and con-
tain a large number of target moments. However, D3G ob-
tains promising performance and outperforms ViGA by a
large margin on TACoS as shown in Table 2.
(2) D3G effectively exploits the information provided by
glance annotation and mines more moments of high qual-
ity for training compared with ViGA. Due to the limitations
of fixed scale Gaussian function and fixed sliding window,
ViGA fails to mine accurate candidate moments to learn
a well-aligned joint embedding space. Instead, D3G gen-
erates a wide range of candidate moments and samples a
group of reliable candidate moments for group contrastive
learning. Compared to ViGA, D3G achieves obvious gains
5.08% and 3.5% at R@1 IoU=0.5 and R@1 IoU=0.7 on
Charades-STA, respectively. Specially, significant improve-
ments are obtained at R@5 on three datasets.
(3) D3G substantially narrows the performance gap be-
tween weakly/glance supervised methods and fully super-
Method
R@1
R@5
IoU=0.5
IoU=0.7
IoU=0.5
IoU=0.7
MAN [41]
41.21
20.54
83.21
51.85
2D-TAN [43]
39.70
23.31
80.32
51.26
SSCS [7]
43.15
25.54
84.26
54.17
MMN [33]
47.31
27.28
83.74
58.41
CRM [12]
34.76
16.37
-
-
CNM [44]
35.43
15.45
-
-
LCNet [38]
39.19
18.87
80.56
45.24
CPL† [45]
32.27
14.22
78.34
43.45
PS-VTG‡ [36]
39.22
20.17
-
-
PFU‡ [15]
54.66
28.34
-
-
VIGA∗[6]
36.56
16.10
48.90
25.86
D3G
41.64
19.60
79.25
49.30
Table 1. Performance comparison on Charades-STA under differ-
ent supervision settings.Top:full supervision, Middle: weak super-
vision, Bottom:glance supervision. †we reproduce the results with
official code and VGG features for fair comparison.
∗we repro-
duce the results with official code for results at R@5.‡ indicates
the method utilizes I3D features.
Method
R@1
R@5
IoU=0.3
IoU=0.5
IoU=0.7
IoU=0.3
IoU=0.5
IoU=0.7
CTRL [9]
18.32
13.30
-
36.69
25.42
-
2D-TAN [43]
37.29
25.32
-
57.81
24.04
-
SSCS [7]
41.33
29.56
-
60.65
48.01
-
MMN [33]
38.57
27.24
-
65.31
50.69
-
MAT [42]
48.79
37.57
-
67.63
57.91
-
VIGA∗[6]
20.82
9.52
3.10
27.92
15.35
6.10
PS-VTG [36]
23.64
10.00
3.35
-
-
-
D3G
27.27
12.67
4.70
54.61
31.34
12.35
Table 2. Performance comparison on TACoS under different super-
vision settings.Top:full supervision, Bottom:glance supervision.
∗we reproduce the results with official code for results at R@5.
vised methods. Specifically, D3G already surpasses previ-
ous method (e.g., CTRL) on both TACoS and ActivityNet
Captions. Undeniably, there are still non-negligible margin
compared to the state-of-the-art fully supervised methods
(e.g., MMN). Note that D3G is very concise and not embed-
ded with auxiliary module (e.g., MLM used in [45]). D3G
still can be enhanced with some complementary modules.
4.4. Ablation Study
To validate the effectiveness of different components of
the proposed D3G and investigate the impact of hyper-
parameters, we perform ablation studies on Charades-STA.
Effectiveness of SA-GCL and DGA. Since Lalign is the
only loss of D3G, to validate the effectiveness of SA-GCL,
we need to simplify the SA-GCL module as a baseline.
Specifically, we only sample the top-1 positive moment to

(a) Effect of top-k.
(b) Effect of σ.
(c) Effect of Tr
Figure 5. Effect of different hyper-parameters on Charades-STA dataset.
Method
R@1
R@5
IoU=0.3
IoU=0.5
IoU=0.7
IoU=0.3
IoU=0.5
IoU=0.7
CTRL [9]
47.43
29.01
10.34
75.32
59.17
37.54
2D-TAN [43]
59.46
44.51
26.54
85.53
77.13
61.96
LGI [20]
58.52
41.51
23.07
-
-
-
SSCS [7]
61.35
46.67
27.56
86.89
78.37
63.78
MMN [33]
65.05
48.59
29.26
87.25
79.50
64.76
MAT [42]
-
48.02
31.78
-
78.02
63.18
CRM [12]
55.26
32.19
-
-
-
-
CNM [44]
55.68
33.33
-
-
-
-
LCNet [38]
48.49
26.33
-
82.51
62.66
-
CPL [45]
53.67
31.24
-
63.05
43.14
-
VIGA∗[6]
59.78
35.39
16.25
72.19
53.19
32.69
PS-VTG [36]
59.71
39.59
21.98
-
-
-
PFU [15]
59.63
36.35
16.61
-
-
-
D3G
58.25
36.68
18.54
87.84
74.21
52.47
Table 3. Performance comparison on ActivityNet Captions under
different supervision settings.Top:full supervision, Middle: weak
supervision, Bottom:glance supervision. ∗we reproduce the results
with official code for results at R@5.
compute the normal contrastive loss (degraded to simpli-
fied MMN) as shown in the first row of Table 4. However,
the top-1 moment tends to be the shortest moment and has
small overlap with target moment, which is decided by the
intrinsic characteristic of 2D-TAN. Therefore, the perfor-
mance of baseline is undoubtedly very poor, which demon-
strates that the main improvement of D3G is not brought
by the backbone of MMN. This phenomenon then encour-
ages us to sample a group of positive moments in SA-GCL.
With full SA-GCL, the model obtains notable performance
gains. Moreover, we introduce the DGA to alleviate annota-
tion bias and model some complex target moments consist-
ing of multiple events. After equipped with DGA, D3G and
simplified D3G achieve obvious performance improvement.
Impact of Sampling Strategy. In SA-GCL, sampling a
group of reliable positive moments is of great importance.
We investigate the impacts of two priors: Gaussian weight
and semantic consistency, respectively. As shown in the first
row of Table 5, we sample top-k positive moments accord-
Module
R@1
R@5
SA-GCL DGA IoU=0.5 IoU=0.7 IoU=0.5 IoU=0.7
✓†
5.08
0.81
14.78
3.36
✓†
✓
13.92
3.31
33.55
11.77
✓
40.51
16.10
74.41
43.31
✓
✓
41.64
19.60
79.25
49.30
Table 4. Effectiveness of SA-GCL and DGA in D3G on Charades-
STA. ✓† denotes an simplified implementation of SA-GCL.
Types
R@1
R@5
GW
SC
IoU=0.5
IoU=0.7
IoU=0.5
IoU=0.7
✓
38.09
16.10
66.53
36.51
✓
25.67
9.57
65.43
38.52
✓
✓
40.51
16.10
74.41
43.31
Table 5. Impact of different strategies used to sample positive mo-
ments for SA-GCL on Charades-STA. GW: Gaussian weight, SC:
semantic consistency.
ing to the Gaussian prior weight. An alternative scheme is
that we sample top-k positive moments according to the se-
mantic consistency scores between candidate moments and
query sentence. However, both of them obtain sub-optimal
performance. This is because Gaussian prior weight is not
always reliable due to the annotation bias and semantic con-
sistence scores are highly dependent on the stability of fea-
tures. Therefore, we finally fuse these two priors to obtain
relatively reliable prior. As shown in the third row of Ta-
ble 5, obvious performance gains are obtained after both of
them are utilized, which demonstrates that these two priors
indeed complement each others.
Effect of different hyper-parameters. As shown in Fig-
ure 5, we investigate three critical hyperparameters in D3G.
As verified in Table 4, sampling enough latent positive mo-
ments is beneficial to mining target moment for training. As
shown in Figure 5 (a), the performance gains increase ob-
viously as the k increases. However, it begins to decrease

Method
R@1
R@5
IoU=0.5
IoU=0.7
IoU=0.5
IoU=0.7
ViGA
36.56
16.10
48.90
25.86
D3G
41.64
19.60
79.25
49.30
ViGA+ 33.66(−2.90) 14.65(−1.45) 47.45(−1.45) 25.51(−0.35)
D3G+
40.19(−1.45) 19.62(+0.02) 78.90(−0.35) 49.41(+0.11)
Table 6. Performance comparison on Charades-STA with extreme
glance annotation.
+ indicates according method is trained with
extreme glance annotations.
after the k reaches a specific value. We argue that selecting
excessive positive moments tends to incorporate some false
positive moments and therefore degrades the performance.
We finally set the k to 10 for Charades-STA, which bal-
ances well the performance and computational cost. As for
hyperparameter σ, it essentially decides the width of Gaus-
sian distribution. A larger σ can well characterize the target
moment of longer duration and vice versa. We vary the σ
from 0.1 to 0.5, and observe that value 0.3 is relatively suit-
able for the Charades-STA dataset. As for hyperparameter
Tr in Eq. (8), it controls the degree of dynamic Gaussian
prior adjustment. We conduct experiments with relevance
thresholds around 0.9. A small threshold tends to introduce
interference while a large threshold fails to find the neigh-
bor frames with consistent semantic. As shown in Figure 5
(c), the moderate threshold 0.9 relatively balances the afore-
mentioned dilemma.
Tolerance to Extreme Glance Annotation. In order to
verify the ability of addressing extreme glance annotation,
we first generate extreme glance annotation, where only the
positions near the start/end timestamps will be sampled as
glance g. As shown in Table 6, both ViGA+ and D3G+
are confronted with the performance degradation at some
metrics(e.g., R@1 IoU=0.5). However, the performances of
D3G are relatively stable compared to ViGA, which demon-
strate that D3G indeed is able to alleviate annotation bias.
4.5. Qualitative Analysis
To clearly reveal the effectiveness of our method, we
visualize some qualitative examples from the test split of
Charades-STA dataset and ActivityNet Captions dataset. As
shown in Figure 6, the proposed D3G achieves more ac-
curate localization of target moment compared to ViGA.
Specifically, ViGA cannot well align the visual content
and semantic information and tend to be disturbed by ir-
relevant content, which may be caused by the annotation
bias. Instead, D3G utilizes SA-GCL and DGA to alleviate
the annotation bias, which enables D3G to well align the
query with the corresponding moment. Moreover, the DGA
adopts multiple Gaussian functions to model target mo-
ment, which is beneficial to representing the complete dis-
tribution of complex moments consisting of multiple events.
As shown in Figure 6 (b), D3G still effectively localizes the
15.56 s – 29.39 s
21.61 s – 34.58 s
Query: person starts playing games on a laptop.
duration: 34.58 s
GT
ViGA
D3G
21.9 s – 34.58 s
(a)
Query: The men cook together while mixing in more
ingredients and presenting it on a plate.
duration: 121.84 s
GT
ViGA
D3G
77.37 s – 121.84 s
17.06 s – 102.35 s
68.53 s – 118.03 s
(b)
Figure 6. Qualitative examples of top-1 predictions. (a) and (b)
is from the Charades-STA dataset and the ActivityNet Captions
dataset, respectively.
GT indicates the ground truth temporal
boundary.
complex moments while ViGA misses the last events “rep-
resent it on a plate”. More qualitative examples will be pro-
vided in Supplementary Materials.
5. Conclusion
In this study, we investigate a recently proposed task,
Temporal Sentence Grounding with Glance Annotation.
Under this setting, we propose a Dynamic Gaussian
prior based Grounding framework with Glance annota-
tion, termed D3G. Specifically, D3G consists of a Semantic
Alignment Group Contrastive Learning module (SA-GCL)
and a Dynamic Gaussian prior Adjustment module (DGA).
SA-GCL aims to mine a wide range of positive moments
and align the positive sentence-moment pairs in the joint
embedding space. DGA effectively alleviates the annota-
tion bias and models complex query consisting of multiple
events via dynamically adjusting the Gaussian prior with
multiple Gaussian functions, promoting the precision of lo-
calization. Extensive experiments show that D3G signifi-
cantly narrows the performance gap between fully super-
vised methods and glance supervised methods. Without ex-
cessive interaction of visual-language, D3G provides a con-
cise framework and a fresh insight to the challenging tem-
poral sentence grounding under low-cost glance annotation.
Limitations. Although D3G achieves promising improve-
ments with glance annotations, it still has some limitations.
In this paper, the DAG adjusts Gaussian prior via the com-
bination of multiple fixed scale Gaussian functions. It fails
to scale down the Gaussian distribution to fit the small mo-
ments. It is expected to explore dynamic learnable Gaussian
functions to model moment of arbitrary duration in future
work. Besides, the sampling strategy for SA-GCL is still
not enough flexible to sample accurate positive moments.

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Appendix
A. Effectiveness of SA-GCL and DGA
To further analyze the effectiveness of SA-GCL and
DGA, we provide more detailed experimental results on
ActivityNet Captions and TACoS datasets as shown in Ta-
ble 7 and Table 8. Following the main manuscript, we re-
gard the simplified implementation of SA-GCL as a base-
line.
After being equipped with the complete SA-GCL,
our model achieves significant improvements on both Ac-
tivityNet Captions and TACoS. This phenomenon demon-
strates that sampling enough positive moments for con-
trastive learning is of great importance. Additionally, we
further incorporate the DGA module for alleviating the an-
notation bias and modeling complex target moments. Since
the ActivityNet Captions dataset has a large number of com-
plex query sentences consisting of multiple events, D3G
obtains notable performance gains on ActivityNet Cap-
tions(e.g. 9.03% at R@5 IoU=0.7). However, TACoS is still
challenging for D3G due to the dense distributions of target
moments.
Module
R@1
R@5
SA-GCL DGA IoU=0.5 IoU=0.7 IoU=0.5 IoU=0.7
✓†
0.83
0.28
1.78
0.58
✓
32.65
16.00
65.48
43.44
✓
✓
36.68
18.54
74.21
52.47
Table 7. Effectiveness of SA-GCL and DAG in D3G on Activi-
tyNet Captions. ✓† denotes an simplified implementation of SA-
GCL.
Module
R@1
R@5
SA-GCL DGA IoU=0.5 IoU=0.7 IoU=0.5 IoU=0.7
✓†
2.97
0.37
5.40
1.10
✓
11.95
4.20
29.07
10.30
✓
✓
12.67
4.70
31.34
12.35
Table 8. Effectiveness of SA-GCL and DAG in D3G on TACoS.
✓† denotes an simplified implementation of SA-GCL.
B. Effect of different hyper-parameters
In this section, we investigate the effect of two criti-
cal hyperparameters on ActivityNet Captions and TACoS
datasets. As shown in Figure 7 and Figure 8, we report
the changes in performance at four metrics. As for top-k,
the performance increases dramatically as the k increases.
However, the performance gradually achieves saturation af-
ter the k reaches 15. We finally select k = 20 for both Ac-
tivityNet Captions and TACoS. As for σ, the ActivityNet
Figure 7. Effect of top-k and σ on ActivityNet Captions dataset.
Captions dataset tends to prefer large values while small
values are more suitable for the TACoS dataset. This is be-
cause the former contains a large number of long target mo-
ments while the latter contains numerous short target mo-
ments. As shown in Figure 7 and Figure 8, we eventually
select σ = 0.6 and σ = 0.2 for ActivityNet Captions and
TACoS for optimal performance, respectively.
C. Qualitative Analysis
In this section, we provide more qualitative examples
from the test split of the Charades-STA dataset, ActivityNet
Captions dataset, and TACoS dataset. For each video, we
select two queries for analysis. As shown in Figure 9 (a),
D3G locates the target moment accurately while ViGA ig-
nores the reason at the front of the target moment, given
Query 1. However, D3G is inferior to ViGA in some cases
such as Query 2. As for complex queries in ActivityNet
Captions, D3G still localizes a moment with a large over-
lap with the target moment. Since sentence-level features
may lose some information about specific events, D3G can-
not perceive accurate boundaries for some complex queries,
such as Figure 9 (b) Query 2. It is expected to explore event-

Figure 8. Effect of top-k and σ on TACoS dataset.
level features for queries consisting of multiple events in
the future. TACoS is the most challenging dataset, where
the videos have long durations and contain a large number
of moment-sentence pairs. As shown in Figure 9 (c), we
observe that D3G fails to locate a simple query of short du-
ration from the long video, given Query 1. However, D3G
accurately locates the target moment of long duration given
Query 2. Note that D3G well attends to the number “the
last two” of the query while ViGA fails to attend to such in-
formation and locates irrelevant moments. As observed in
Figure 9, D3G is superior to ViGA, which is consistent with
the experimental results in the main manuscript. However,
D3G still has some limitations and needs to be improved in
the future.

Query 1: person laughing because they see something funny on the television.
0 s – 5.2 s
4.03 s – 14.1 s
0 s – 6.29 s
Query 2: a person in their dining room is running around.
1.3 s – 12.8 s
1.01 s – 11.07 s
1.57 s – 7.87 s
Query 1: A man and a woman are standing outside at a beach in the sand talking while the lady
holds a brown paper bag in her hand and a man begins filming them.
duration: 213.42 s
0 s – 44.82 s
0 s – 64.03 s
0 s – 53.35 s
Query 2: The teams begin to get extremely individual and add words and feathers to their
masterpiece before the man and lady come around to judge them.
138.72 – 185.67 s
57.62 s – 164.33 s
120.05 s – 213.42 s
…
5.17 s – 10.03 s
0 s – 37.91 s
8.89 s – 26.66 s
Query 1:The person gets out a cutting board.
Query 2: The person cuts up the last two slices of pineapple.
duration: 379.11 s
327.14 s – 372.11 s
185.77 s – 242.63 s
346.54 s – 376.15 s
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
GT
ViGA
D3G
(a)
(b)
(c)
duration: 25.17 s
Figure 9. Qualitative examples of top-1 predictions. (a), (b) and (c) is from the Charades-STA dataset, the ActivityNet Captions and the
TACoS dataset, respectively. GT indicates the ground truth temporal boundary.

Probability Distribution Based Frame-supervised
Language-driven Action Localization
Shuo Yang
shuoyang@bit.edu.cn
Beijing Key Laboratory of Intelligent
Information Technology, Beijing
Institute of Technology
Guangdong Laboratory of Machine
Perception and Intelligent Computing,
Shenzhen MSU-BIT University
Zirui Shang
ziruishang@bit.edu.cn
Beijing Key Laboratory of Intelligent
Information Technology, Beijing
Institute of Technology
Xinxiao Wu∗
wuxinxiao@bit.edu.cn
Beijing Key Laboratory of Intelligent
Information Technology, Beijing
Institute of Technology
Guangdong Laboratory of Machine
Perception and Intelligent Computing,
Shenzhen MSU-BIT University
ABSTRACT
Frame-supervised language-driven action localization aims to lo-
calize action boundaries in untrimmed videos corresponding to the
input natural language query, with only a single frame annotation
within the target action in training. This task is challenging due to
the absence of complete and accurate annotation of action bound-
aries, hindering visual-language alignment and action boundary
prediction. To address this challenge, we propose a novel method
that introduces distribution functions to model both the probability
of action frame and that of boundary frame. Specifically, we assign
each video frame the probability of being the action frame based on
the estimated shape parameters of the distribution function, serv-
ing as a foreground pseudo-label that guides cross-modal feature
learning. Moreover, we model the probabilities of start frame and
end frame of the target action using different distribution functions,
and then estimate the probability of each action candidate being
a positive candidate based on its start and end boundaries, which
facilitates predicting action boundaries by exploring more positive
terms in training. Experiments on two benchmark datasets demon-
strate that our method outperforms existing methods, achieving a
gain of more than 10% of 𝑅1@𝜇≥0.5 on the challenging TACoS
dataset. These results emphasize the significance of generating
pseudo labels with appropriate probabilities via distribution func-
tions to address the challenge of frame-supervised language-driven
action localization. 1
CCS CONCEPTS
• Information systems →Novelty in information retrieval;
Video search.
∗corresponding author
1Codes could be found at github
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https://doi.org/10.1145/3581783.3612512
KEYWORDS
language-driven action localization, video moment retrieval, distri-
bution, frame-supervised.
ACM Reference Format:
Shuo Yang, Zirui Shang, and Xinxiao Wu. 2023. Probability Distribution
Based Frame-supervised Language-driven Action Localization. In Proceed-
ings of the 31st ACM International Conference on Multimedia (MM ’23), Octo-
ber 29–November 3, 2023, Ottawa, ON, Canada. ACM, New York, NY, USA,
10 pages. https://doi.org/10.1145/3581783.3612512
1
INTRODUCTION
Language-driven action localization has drawn increasing atten-
tion in recent years, which aims to locate the action interval in an
untrimmed video that is semantically relevant to a language query.
This task, also known as video moment retrieval [7, 34, 39, 46] or
temporal sentence grounding [16, 18, 44], is a fundamental prob-
lem in video understanding and multi-modal information retrieval,
which involves not only cross-modal alignment but also action
boundary localization. It has been widely applied in various sce-
narios, such as content-based video search and automatic video
editing.
Previous methods [1, 6, 11, 31, 37, 43] have achieved remarkable
success in the fully-supervised setting that requires annotating both
the start and end timestamps of the target action corresponding to a
given language query, as shown in Figure 1 (a). However, the frame
annotation paradigm is time-consuming because annotators need
to review videos multiple times to accurately identify action bound-
aries. Consequently, recent methods [13, 25, 33, 38, 50] explore the
weakly-supervised setting where only language-video pair annota-
tions are provided, resulting in less annotation burden but lower
performance, as shown in Figure 1 (b). The frame-supervised set-
ting, first proposed in [4, 42], uses single-frame annotation within
the target action, achieving a good balance between annotation
cost and performance, as shown in Figure 1 (c). However, incom-
plete annotations still hinder visual-language alignment and action
boundary prediction.
In this paper, we propose a new method to model the probabilities
of action frames and boundary frames by introducing distribution
functions. By exploiting the temporal consistency between video
frames and properties of probability distribution functions, we ex-
tend annotated frames to other frames with different probabilities.
This enables the generation of frame-wise pseudo-labels of action
frames, which is useful for learning video-language alignment. We
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MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada.
Shuo Yang, Zirui Shang, and Xinxiao Wu
Language query: person opens a refrigerator
Language query: person opens a refrigerator
Language query: person opens a refrigerator
10.5s
18.1s
12.6s
(b) Weakly-supervised
(c) Frame-supervised
(a) Fully-supervised
Figure 1: Illustration of different settings of language-driven
action localization. Given an input language query and an
input video, (a) the fully-supervised setting provides the start-
ing and ending boundaries of the target action, (b) the weakly-
supervised setting provides no additional labels, and (c) the
frame-supervised setting gives a frame annotation within
the target action.
also model the probabilities of a video frame as the start and end of
the target action via different distribution functions. And by multi-
plying the start and end probabilities, we can obtain the probability
of an action candidate being a positive target action segment. By
doing so, all action candidates can be treated as positive action can-
didates with varying probabilities, enabling the exploration of all
possible positive action candidates with appropriate probabilities
and thus improving the accuracy of boundary estimation.
Specifically, we assign each video frame a probability of being
the action frame, which is highest at the annotated frame and grad-
ually decreases to a minimum near the boundary. To model this
probability, we estimate the parameters of a specific distribution
based on the visual similarity and temporal distance between the
video frames and the annotated frame. In particular, we use the
various asymmetrical probability curves of Beta distribution to han-
dle the situations in which only few annotated frames are located
at the center of the action segments. Using the resulting probabil-
ity as a soft label for each action frame, we can optimize a binary
cross-entropy loss that forces the visual feature to be similar to the
language query feature with appropriate loss weights.
Furthermore, we introduce another distribution function to model
the probability of each video frame being the start or end bound-
ary of the target action. In our method, the highest probabilities
are assigned to the boundaries of an action candidate, or so-called
proposal, that is closest to the language query in the feature embed-
ding space, while the annotated frame is less likely to be an action
boundary, as illustrated in Figure 4. We combine the probabilities
of the starting and ending boundaries to give each action candidate
a probability of being the target action segment. This helps us iden-
tify more positive action candidates with reasonable probabilities,
thereby facilitating the localization of action boundaries.
The main contributions of this paper are as follows:
• We propose a novel method that uses distribution functions,
such as the Beta distribution, to generate a probability for
each video frame being the action frame, serving as a pseudo-
label to enhance the cross-modal feature learning.
• We propose to use different distribution functions to model
the probabilities of the start frame and end frame of the
target action, so as to explore more positive action candidates
during training, thus facilitating the localization of action
boundaries.
• Experiments on two benchmark datasets demonstrate that
our method outperforms existing methods, especially achiev-
ing a gain of more than 10% of 𝑅1@𝜇≥0.5 on the challeng-
ing TACoS dataset.
2
RELATED WORK
Current language-driven action localization settings can be roughly
divided into three types: fully-supervised, weakly-supervised and
frame-supervised.
Fully-supervised language-driven action localization re-
quires the annotation of start and end timestamps for each query
during training. Existing methods of this setting can be broadly
categorized into two groups: proposal-based and proposal-free. In
the proposal-based methods, candidate proposals are first generated
using sliding windows, proposal generation, or anchor-based meth-
ods, and are then ranked based on queries. For instance, CTRL [6],
MCN [1], MARN [19], HVSARN [20] and TSTNet [40] generate pro-
posals of varying lengths through sliding windows. 2D-TAN [48],
MGPN [32], HLN [5] and VDI [22] generate proposals by using
a two-dimensional feature map that model the relationships be-
tween segments of varying durations. The proposal-free methods
directly predict the start and end boundaries of the target action
on sequences of fine-grained video clips. According to the format
of moment boundaries, proposal-free methods are categorized into
span-based and regression-based methods. VSLNet [47], SLP [14]
and D-TSG [17] directly predict the probability of each video snip-
pet or frame being the start and end positions of the target action.
TVP [49] and MGSL-Net [15] calculates a time pair and compares
it with ground truth for model optimization.
Weakly-supervised language-driven action localization
only requires the annotation of pairs of video and query instead of
the annotation of start and end times, thus reducing the high an-
notation cost. Existing weakly-supervised language-driven action
localization methods can be broadly classified into two categories:
multi-instance learning and reconstruction-based methods. For in-
stance, TGA [25] regards the video and its corresponding query de-
scriptions as positive pairs, while considering the video with other
queries and the query with other videos as negative pairs. This
method learns video-level visual-text alignment by maximizing the
matching scores of positive samples while minimizing the scores
of negative samples. SAN [38] introduces a multi-scale Siamese
module that progressively narrows the semantic gap between the
visual and textual modalities. RTBPN [50] uses a language-aware
filter to generate an enhanced video stream and a suppressed video
stream, which are used to generate positive proposals and negative
proposals for sufficient confrontment, separately.
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Distribution Based Frame-supervised Language-driven Action Localization
MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada.
Frame-supervision is a setting that aims to strike a balance
between annotation cost and performance, which has been applied
to various computer vision tasks. Bearman et al. [2] introduce the
concept of frame supervision for semantic segmentation. Mettes
et al. [24] extend the use of frame supervision to spatio-temporal
action localization in videos. In recent years, Ma et al. [23] pro-
pose a SF-Net model for video temporal action localization by using
single-frame supervision. Li et al. [12] develop a temporal action seg-
mentation model that requires only timestamp annotations. More
recently, some studies investigated the implementation of single-
frame annotation for language-driven action localization. Cui et
al. [4] originally introduce the concept of frame supervision for
language-driven action localization, which uses the Gaussian distri-
bution to model the probability distribution of foreground frames.
Meanwhile, Xu et al. [42] employ a combination of Language Acti-
vation Sequence (LAS) and given frame supervision to enhance the
model’s ability in language-driven action localization.
3
OUR METHOD
3.1
Problem Definition
Given an untrimmed video and a language query, the task of frame-
supervised language-driven action localization aims to localize the
target action boundaries (𝜏𝑠,𝜏𝑒) with an additional frame annota-
tion 𝑡𝑝in the training stage, where 𝜏𝑠≤𝑡𝑝≤𝜏𝑒, and 𝜏𝑠and 𝜏𝑒
represent the start and end frames of the action corresponding to
the language query, respectively. Note that in the inference stage,
the frame annotation is not available.
3.2
Baseline Model
Due to the lack of boundary annotation, we propose a baseline
model of frame-supervised language-driven action localization,
which follows an multiple-instance learning (MIL) strategy and
consists of three components: a video encoder, a language encoder,
and a cross-modal interaction module, as shown in Figure 2.
Video Encoder. We first split the given video into a sequence
of non-overlap clips with a fixed length (e.g., 16 frames) and ex-
tract visual features of each clip by a pre-trained 3D-CNN [3, 35].
Then we uniformly sample 𝑇features and project them into 𝑑-
dimensional representations using a fully-connected (FC) layer.
Finally, we encode temporal relationships using a standard Trans-
former block [36] that consists of multi-head self-attention, layer
normalization, residual connection, and feed-forward network. The
process of video encoding is represented by
𝑽′ = 𝑇𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑒𝑟(𝐹𝐶(Uniform-Sample(3D-CNN(𝑉)))).
(1)
Language Encoder. Given an input language query, we first ini-
tialize its word features using the GloVe embedding [28] and then
project their dimension to 𝑑by a fully-connected (FC) layer, fol-
lowed by a three-layer bi-directional Gated Recurrent Unit (GRU)
to learn the relationships of words:
𝑸′ = Bi-GRU(𝐹𝐶(𝐺𝑙𝑜𝑉𝑒(𝑄))).
(2)
Cross-modal Interaction Module. After encoding the video and
language query, we adopt two cross-attention modules for the cross-
modal interaction, each by regarding one modality as the query
and the other as key and value, followed by a layer normalization,
a residual connection and a feed-forward network:
𝑭𝑣= 𝐹𝐹𝑁(𝐿𝑁(𝑀𝑆𝐴(𝐹𝐶𝑄(𝑽′), 𝐹𝐶𝐾(𝑸′), 𝐹𝐶𝑉(𝑸′)) + 𝑽′)),
𝑭𝑞= 𝐹𝐹𝑁(𝐿𝑁(𝑀𝑆𝐴(𝐹𝐶𝑄(𝑸′), 𝐹𝐶𝐾(𝑽′), 𝐹𝐶𝑉(𝑽′)) + 𝑸′)),
(3)
where 𝑀𝑆𝐴is the multi-head self-attention module[36]; 𝐿𝑁de-
notes layer normalization; 𝐹𝐶𝑗(·) denotes fully connected layer,
𝑗∈{𝑄, 𝐾,𝑉}; 𝐹𝐹𝑁(·) is a feed-forward network.
Training loss. For the learned video features 𝑭𝑣, we generate ac-
tion candidates 𝑃= {𝑃1, 𝑃2, · · · , 𝑃𝑁}, also known as proposals, by
sliding windows, where 𝑁is the total number of action candidates,
and 𝑃𝑖= max-pooling([𝑭𝑣,𝑠𝑖, · · · , 𝑭𝑣,𝑒𝑖]) ∈R𝑑is the 𝑖-th action
candidate and 𝑠𝑖and 𝑒𝑖are its start and end frame index, respec-
tively; And we also compute the sentence features by mean-pooling
on the learned language features: 𝑭𝑠= mean-pooling(𝑭𝑞) ∈R𝑑.
In the training stage, we use an intra-video loss and an inter-
video loss to learn the video-language alignment. The intra-video
loss treats the action candidates containing the annotated frame
as positive candidates 𝑃+ and others as negatives 𝑃−. It enforces
the similarities between the language query and positive action
candidates larger than the similarities between the language query
and negative candidates by the InfoNCE [27] loss, given by
L𝑖𝑛𝑡𝑟𝑎= −1
𝑀
∑︁
𝑝𝑖∈𝑃+
log
exp(𝑆(𝑝𝑖, 𝑭𝑠)/𝜏)
exp(𝑆(𝑝𝑖, 𝑭𝑠)/𝜏) +
∑︁
𝑝𝑗∈𝑃−
exp(𝑆(𝑝𝑗, 𝑭𝑠)/𝜏)
,
(4)
where 𝑆(·, ·) is the cosine similarity function; 𝑀is the number of
positive action candidates; 𝜏is a temperature parameter and set to
0.07 as ViGA [4].
The inter-video loss is also an InfoNCE loss and calculated in a
mini-batch, where the positive candidates 𝑃+ in the paired video-
sentences are positive terms, and all action candidates of unpaired
video-sentences are negative terms. The inter-video enforces the
similarities between positive terms larger than the similarities be-
tween negative terms in a mini-batch, given by
L𝑖𝑛𝑡𝑒𝑟= −
1
𝑀×𝐵
𝐵
∑︁
𝑏=0
∑︁
𝑝𝑏,𝑖∈𝑃+
𝑏
log
exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠
𝑏)/𝜏)
exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠
𝑏)/𝜏) + N ,
N =
∑︁
𝑗≠𝑏

exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠
𝑗)/𝜏) + exp(𝑆(𝑝𝑗, 𝑭𝑠
𝑏)/𝜏)

,
(5)
where 𝑝𝑏,𝑖∈𝑃+
𝑏is the 𝑖-th positive candidates of 𝑏-th video in
a mini-batch; 𝑀is the size of 𝑃+
𝑏; 𝐵is batch size; N denotes the
negative terms of none paired video-sentence in the mini-batch.
3.3
Pseudo-label of Action Frame
The baseline model uses frame annotations to distinguish positive
and negative action candidates, which ignores the temporal coher-
ence of videos. Indeed, the annotated frames play a pivotal role in
the frame-supervised language-driven action localization. A com-
mon intuition is that the frames adjacent to the annotated frame
are more likely to be action frames, while the frames far from the
annotated frame are less likely to be action frames. However, how
these possibilities change remains an open problem.
In this study, we propose to use distribution functions to model
the probability changes, taking into account temporal distance and
visual similarity between video frames. The resulting probabilities
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MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada.
Shuo Yang, Zirui Shang, and Xinxiao Wu
Fq
Language query:
person opens a
refrigerator
Video
Encoder
Language
Encoder
Cross-modal
Interaction
Fv
Candidates
……
A
Probability of Action
boundary
s’
e’
start
end
Intra-loss
Inter-loss
Candidate
labels
Probability of
Action frame
A
Action frame labels
Fore-loss
Fs
Distribution based loss
Baseline model
Figure 2: Overview of the proposed method. In the distribution based loss, 𝐴denotes the annotated frame; 𝑠
′ and 𝑒
′ are the
boundaries of the candidate with maximum similarity to the language query; the curves represent the probabilities by the
estimated distributions, by which the pseudo labels of action frames and action candidates are generated (dark color means
high probability).
Probability of
Action frame
η
1
0
Probability of
Action frame
η
1
0
Probability of
Action frame
A
τs
τe
η
1
0
T
(a) Triangular
(b) Gaussian
(c) Beta
A
τs
τe
T
A
τs
τe
T
Figure 3: Probabilities of target action frames by different
distributions: (a) Triangular Distribution, (b) Gaussian Distri-
bution, and (c) Beta Distribution. 𝜏𝑠and 𝜏𝑒denote the ground-
truth boundary of target action; 𝐴is the annotated frame; 𝑇
is the video length; 𝜂is the minimum probability.
can be viewed as pseudo-labels for action frames, which extends
the annotated frame to its neighbors with different probabilities.
This extension provides valuable guidance for learning cross-modal
alignment, thereby improving the accuracy of action localization.
By leveraging distribution functions in this way, we aim to improve
the performance of action localization in videos. Here we explore
three distributions to model the probability of frames being the
action frames.
Triangular distribution. We start with a simple distribution, the
Triangular distribution, which models the probability changes based
only on the temporal distance between frames. In this distribution,
the probability decreases linearly with the increasing distance from
the annotated frame. As shown in Figure 3(a), we assume that the
minimum probability of vidoe frame is 𝜂, i.e., 𝜂= 1
𝑇, where 𝑇is
the length of video, and the maximum probability of the annotated
frame 𝐴is 1, the probability of the frame 𝑥being the action frame
is calculated by
𝑃𝑓
𝑡(𝑥) =
( 1
𝐷( 𝑥(1−𝜂)
𝐴
+ 𝜂),
0 ≤𝑥≤𝐴
1
𝐷( (𝑇−𝑥) (1−𝜂)
(𝑇−𝐴)
+ 𝜂),
𝐴< 𝑥≤𝑇
(6)
where the factor 1
𝐷is used as a normalization factor to ensure that
the sum of all probabilities is equal to 1. However, in cases where
we want the maximum probability to be 1, we can discard this
normalization factor by setting 𝐷= 1 without affecting the shape
of the distribution.
Gaussian distribution. The Gaussian distribution can also model
the probability changes of action frames. This more sophisticated
distribution assumes that the probability changes follow a bell curve,
with the highest probability occurring at the annotated frame and
gradually decreasing as the distance from the annotated frame in-
creases, as illustrated in Figure 3(b). The mean 𝜇of the Gaussian
distribution is set to the annotated frame 𝐴, and we estimate its vari-
ance 𝜎2 by considering both the temporal distance and visual simi-
larity between frames. The temporal distance between the frame 𝑡
and the annotated frame 𝐴is 𝐷(𝑡,𝐴) = |(𝑡−𝐴)|
𝑇
. The visual similar-
ity is calculated by𝑉(𝑡,𝐴) = 0.5×𝑐𝑜𝑠𝑖𝑛𝑒_𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑭𝑣𝑡, 𝑭𝑣𝐴)+0.5,
and finally, the similarity is a weighted sum of temporal distance
and visual similarity:
𝑆𝐼𝑀(𝑡,𝐴) = 𝜆1 · 𝑉(𝑡,𝐴) + 𝜆2 · (1 −𝐷(𝑡,𝐴)),
(7)
where 𝜆1 and 𝜆2 are hyper-parameters. The standard deviation 𝜎
can be estimated by 𝜎= (𝐶𝑙+ 𝐶𝑟)/2, where 𝐶𝑙and 𝐶𝑟denote the
number of frames on the left side (𝑡< 𝐴) and right side (𝑡> 𝐴) of the
annotated frame, respectively. These frames satisfy the condition
𝑆𝐼𝑀(𝑡,𝐴) ≥𝜃, where 𝜃is a hyper-parameter set to 0.9 times the
maximum value of 𝑆𝐼𝑀(𝑡,𝐴). With 𝜇and 𝜎, the probability of the
video frames 𝑥being the action frame is calculated by
𝑃𝑓
𝑔(𝑥) = exp(−(𝑥−𝜇)2
2𝜎2
).
(8)
Since the annotated frame is not always located at the center of the
action, the probability changes of the frames on either side of the
annotated frame may not be symmetrical. However, the Gaussian
distribution assumes that the changes are symmetrical, which limits
its accuracy in certain situations.
Beta distribution. To overcome the limitation of the Gaussian
distribution, we introduce the Beta distribution. As shown in Fig-
ure 3(c), the diverse curve and asymmetric probability of the Beta
distribution enables modeling different relative positions of the
annotated frame within an action. The probability density function
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Distribution Based Frame-supervised Language-driven Action Localization
MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada.
Probability of
boundary frame
A
s’
τe
η
1
0
T
(a) Triangular
τs
e’
Probability of
boundary frame
η
1
0
(c) Beta
A
τs
τe
T
s’
e’
Probability of
boundary frame
A
s’
τe
η
1
0
T
(b) Gaussian
τs
e’
Figure 4: Probabilities of start frames (red curves) and end
frames (blue curves) of the target action by the (a) Triangular
Distribution, (b) Gaussian Distribution, and (c) Beta Distri-
bution. 𝑠
′ and 𝑒
′ denote the start and end boundaries of the
selected action candidate, whose similarity with language
query is the largest;
of the Beta distribution is an exponential function of the variable 𝑥
and its reflection (1 −𝑥) as follows:
𝑓(𝑥;𝛼, 𝛽) = 𝑥𝛼−1(1 −𝑥)𝛽−1
B(𝛼, 𝛽)
for 0 ≤𝑥≤1,
(9)
where 𝛼and 𝛽are the shape parameters, and B(𝛼, 𝛽) is the beta
function serving as a normalization factor to ensure the total prob-
ability is 1. The mean 𝜇and variance 𝜎2 are calculated by
𝜇=
𝛼
𝛼+ 𝛽,
𝜎2 =
𝛼𝛽
(𝛼+ 𝛽)2(𝛼+ 𝛽+ 1) .
(10)
As the work [41] says, it is hard to directly estimate the parameters
𝛼and 𝛽. Thus, we estimate the parameters of Beta Distribution by
its mean and variance. To do so, we again calculate the similarity
between the video frame and the annotated frame by Eq.(7), and
set the similarity value larger than the threshold 𝜃to 1 and others
to minimum probability 𝜂, same as Triangular distribution, we set
𝜂= 1
𝑇, where 𝑇is the length of video:
𝑆𝐼𝑀(𝑥,𝐴)
′ =
(
1,
𝑆𝐼𝑀(𝑥,𝐴) ≥𝜃
𝜂,
𝑜𝑡ℎ𝑒𝑟𝑠
(11)
Then the mean ¯
𝜇and variance ¯
𝜎2 are calculated by
¯
𝜇= 1
𝑇
𝑇
∑︁
𝑥=1
𝑆𝐼𝑀(𝑥,𝐴)
′ · 𝑥
𝑇,
¯
𝜎2 = 1
𝑇
𝑇
∑︁
𝑥=1
𝑆𝐼𝑀(𝑥,𝐴)
′ · ( 𝑥
𝑇−𝜇)2.
(12)
Finally, the parameters of the Beta distribution are derived as
𝛼= ¯
𝜇
¯
𝜇(1 −¯
𝜇)
¯
𝜎2
−1

,
𝛽= (1 −¯
𝜇)
¯
𝜇(1 −¯
𝜇)
¯
𝜎2
−1

(13)
Therefore, the probability of the video frames 𝑥being the action
frame is calculated by
𝑃𝑓
𝑏(𝑥) =
1
B(𝛼, 𝛽)
𝑥
𝑇
𝛼−1
1 −𝑥
𝑇
𝛽−1
.
(14)
In cases where we want the maximum probability to be 1, we replace
the normalization factor B(𝛼, 𝛽) with a min-max normalization. In
general, the Beta distribution is more complex to estimate than other
distributions, which may limit its applications in some scenarios.
Nonetheless, this distribution is a valuable tool for modeling the
probability of action frames.
Foreground Loss. With the probabilities of video frames being
the action frames, we introduce a foreground loss to enforce the
embedding of the relevant video frame close to the language query,
which helps to learn cross-modal alignment, given by
L𝑓𝑜𝑟𝑒= 𝐵𝐶𝐸(𝑐𝑜𝑠𝑖𝑛𝑒_𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑭𝑞, 𝑭𝑣)/𝜏, 𝑃𝑓
𝑑),
(15)
where 𝑭𝑞and 𝑭𝑣are the language features and video features,
respectively; 𝐵𝐶𝐸is the binary cross entropy loss; 𝜏is the tempera-
ture parameter; 𝑃𝑓
𝑑with 𝑑∈{𝑡,𝑔,𝑏} are the probabilities of action
frames computed by the Triangular, Gaussian, or Beta distributions.
3.4
Pseudo-label of Boundary Frame
In the baseline model of frame-supervised language-driven action
localization, all action candidates containing the annotated frame
are treated as positive, and others are negative, which may intro-
duce significant noise. As such, we propose to use distribution
functions to model the probability of each video frame being a po-
tential starting or ending boundary of the target action moment. By
modeling the temporal boundaries in this manner, each action can-
didate is assigned a probability of being the target action moment
by multiplying the probabilities of its boundaries.
Given that the annotated frame is inclined to be within the target
action, it is expected to have a minimum probability as a boundary.
And for the positive action candidates 𝑃+ that include the annotated
frame, we designate the one with the maximum cosine similarity
to the language query as the pseudo target action segment, whose
staring and ending boundaries are denoted as 𝑠′ and 𝑒′ in Figure 4,
and assign it a maximum probability of 1. We formulate the starting
boundary probability within the interval [0,𝐴] and the ending
boundary probability within the interval [𝐴,𝑇]. As discussed in
Section 3.3, the starting probability 𝑃𝑠
𝑑and the ending probability
𝑃𝑒
𝑑are modeled using distribution functions, and details are omitted
here. Consequently, the probability of an action candidate being
positive (i.e., belonging to the target action) is computed by taking
the multiplication of its boundary probabilities,given by
𝑃𝑝
𝑑(𝑖) = 𝑃𝑝
𝑑(𝑥1,𝑥2) = 𝑃𝑠
𝑑(𝑥1) × 𝑃𝑒
𝑑(𝑥2),
𝑑∈{𝑡,𝑔,𝑏},
(16)
where 𝑥1 and 𝑥2 are the starting and ending frame indexes of the
𝑖-th action candidate, and 𝑑denotes the index of distributions.
As we assign each action candidate a probability by Eq.(16), we
update the loss function in the baseline model. Specifically, we use
the 𝑃𝑝
𝑑(𝑖) as the loss weight, and accordingly the intra-video and
inter-video losses are re-written as
L
′
𝑖𝑛𝑡𝑟𝑎= −1
𝑀
∑︁
𝑝𝑖∈𝑃+
𝑃𝑝
𝑑(𝑖) · log
exp(𝑆(𝑝𝑖, 𝑭𝑠)/𝜏)
exp( 𝑆(𝑝𝑖,𝑭𝑠)
𝜏
) +
∑︁
𝑝𝑗∈𝑃−
exp( 𝑆(𝑝𝑗, 𝑭𝑠)
𝜏
)
,
(17)
L
′
𝑖𝑛𝑡𝑒𝑟= −
1
𝑀· 𝐵
𝐵
∑︁
𝑏=0
∑︁
𝑝𝑏,𝑖∈𝑃+
𝑏
𝑃𝑝
𝑑(𝑖) · log
exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠
𝑏)/𝜏)
exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠
𝑏)/𝜏) + N .
(18)
3.5
Inference
In the inference stage, we first compute the cosine similarity be-
tween the video features and language features and then filter out
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Shuo Yang, Zirui Shang, and Xinxiao Wu
the action candidates that do not contain the top-k frames based
on their similarity scores. After filtering, we rank all the remaining
action candidates to determine the most likely one. This method
allows us to efficiently identify the action in the video correspond-
ing to the language query by considering both frame and segment
features.
4
EXPERIMENTS
4.1
Datasets
To evaluate the proposed method, we conduct experiments on two
benchmark datasets, including the TACoS and the Charades-STA
datasets.
The TACoS dataset is built on the MPII Cooking Compositive
dataset [29], which consists of 127 videos with an average length
of 4.79 minutes. There are around 148 annotated segments per
video. The dataset contains 18,818 samples, including 10,146 for
training, 4,589 for validation, and 4,083 for testing. This dataset is
more challenging due to the long videos and short action segments.
The Charades-STA dataset is built on the Charades dataset [30]
and contains 6,672 daily life videos. The average duration of the
videos is 29.76 seconds. There are about 2.4 annotated segments per
video, whose average duration is 8.2 seconds. The whole dataset
contains 16,128 samples (i.e., pairs of query and action segment),
and we follow the standard split of 12,408 and 3,720 samples for
training and testing.
4.2
Evaluation Metrics
We adopt two metrics for the performance evaluation: (1)𝑅@𝑛; 𝐼𝑜𝑈≥
𝜇, which denotes the recall of top-𝑛predictions at various thresh-
olds of the temporal Intersection over Union (IoU). It measures the
percentage of predictions that have IoU with ground truth larger
than the threshold 𝜇; (2) mean averaged IoU (mIoU), which de-
notes the average IoU over all the test samples. We set 𝑛= 1 and
𝜇∈{0.3, 0.5, 0.7}.
4.3
Implementation Details
We use C3D [35] for the TACoS dataset and I3D [3] for the Charades-
STA dataset to extract video features. Adam [10] is adopted for
optimization with an initial learning rate of 1e-4 and half decaying
on plateau. The intermediate feature dimension 𝑑is set to 512, and
the head number of multi-head self-attention is set to 8. The hyper-
parameters 𝜆1 and 𝜆2 in Eq.(7) are set to 0.2 and 1 for Charades-STA,
and 0.6 and 0.8 for TACoS. The loss weights for all loss items are
set to 1.
4.4
Ablation Studies
We perform in-depth analysis to evaluate each component of our
method on the TACoS and Charades-STA datasets.
Effectiveness of different distributions. We perform an abla-
tion study using the Triangular, Gaussian, and Beta distributions
to demonstrate the effectiveness of incorporating different distri-
butions into the baseline model. The results on the TACoS and
Charades-STA datasets are shown in Table 1 and Table 2, respec-
tively. It is obvious that each of these distributions significantly
improves the performance of the baseline model on the TACoS
Table 1: Ablation studies of different distributions on the
TACoS dataset.
Methods
𝑅@1; 𝐼𝑜𝑈≥𝜇
𝑚𝐼𝑜𝑈
0.3
0.5
0.7
Baseline
17.05
6.45
1.87
15.47
Ours (Triangular)
34.64
19.02
6.47
22.23
Ours (Gaussian)
35.87
19.47
6.95
22.85
Ours (Beta)
36.14
20.17
7.30
23.09
Table 2: Ablation studies of different distributions on the
Charades-STA dataset.
Methods
𝑅@1; 𝐼𝑜𝑈≥𝜇
𝑚𝐼𝑜𝑈
0.3
0.5
0.7
Baseline
70.4
45.05
20.03
44.30
Ours (Triangular)
66.94
42.63
19.22
42.67
Ours (Gaussian)
71.10
48.15
25.65
46.75
Ours (Beta)
71.72
50.13
26.72
47.35
dataset, and similar trends can be seen on the Charades-STA dataset
except for the Triangular distribution, where the Triangular distri-
bution gives small probabilities to the frame near the ground-truth
due to the short videos. Specifically, on the TACoS dataset, the
Triangular distribution improves the 𝑅@1; 𝐼𝑜𝑈≥0.3 by 17.59%, the
Gaussian distribution improves the 𝑅@1; 𝐼𝑜𝑈≥0.3 by 18.82%, and
the Beta distribution achieves the greatest improvement with the
gain of 19.09% on the 𝑅@1; 𝐼𝑜𝑈≥0.3. These results demonstrate
that the pseudo-labels with appropriate probabilities provide pos-
itive guidance for learning cross-modal alignment and boundary
estimation.
Effectiveness of the Beta distribution. The asymmetric nature
of the Beta distribution makes it well-suited to handle the annotated
frames that occur near the action boundaries. To evaluate the effec-
tiveness of the Beta distribution, we conduct experiments where
the training videos are re-annotated by placing annotated frames
at various positions within the video. For example, the annotated
frames are at the first ten percent of the action segment, which we
denote as 0.1 in Table 3. We re-implement ViGA [4] by re-training
the model using the new annotated frames via the open-source
codes for comparison with our method. The comparison results are
reported in Table 3.
Our results show consistent improvements in 𝑅@1; 𝐼𝑜𝑈≥0.5
and 𝑅@1; 𝐼𝑜𝑈≥0.7, regardless of whether the annotation is located
in the center of the action (represented as 0.5 in Table 3) or near the
action boundaries (represented as 0.1 and 0.9 in Table 3). However,
we observed fewer improvements when the annotations were near
the boundaries, such as at ten (0.1) and ninety (0.9) percentages
of the action segment. This can be attributed to the difficulty in
estimating the distribution parameters accurately in such situations.
We also noticed a slight decrease (less than 1%) in performance on
𝑅@1; 𝐼𝑜𝑈≥0.3, which we believe may be due to the neglect of some
information when the probabilities for certain video frames are
lower. Nonetheless, we are encouraged by the overall effectiveness
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Distribution Based Frame-supervised Language-driven Action Localization
MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada.
Table 3: Ablation studies of different annotation positions 𝑃
on the Charades dataset. The value format 𝑎/𝑏in the table
denotes that 𝑎is the result of the re-trained ViGA [4] and 𝑏
is the result of our method.
P
𝑅@1; 𝐼𝑜𝑈≥𝜇
𝑚𝐼𝑜𝑈
0.3
0.5
0.7
0.1
65.59/65.05
41.42/43.39
20.13/21.64
42.44/42.80
0.3
69.25/70.13
46.05/48.60
20.73/25.38
44.19/46.46
0.5
71.88/71.53
44.97/47.98
21.26/23.28
45.01/45.93
0.7
68.39/68.09
44.41/45.24
19.41/21.34
43.12/43.77
0.9
61.77/61.05
36.29/36.99
15.32/16.75
38.75/38.90
Table 4: Ablation studies of different components on the
Charades-STA dataset. “r1i3” is the short of 𝑅@1; 𝐼𝑜𝑈≥0.3. “v-
s” is the short of visual similarity. “intra” and “inte” represent
the intra-video loss and the inter-video loss, respectively.
v-s
intra
inter
r1i3
r1i5
r1i7
𝑚𝐼𝑜𝑈
1
x
x
x
70.4
45.05
20.03
44.30
2
✓
x
x
71.32
47.77
22.5
45.44
3
x
✓
x
70.43
47.58
24.7
46.05
4
x
x
✓
71.34
45.11
20.65
44.85
5
✓
x
✓
70.97
47.93
23.47
45.85
6
x
✓
✓
71.53
47.98
24.73
46.58
7
✓
✓
x
71.13
49.14
25.91
47.06
8
✓
✓
✓
71.72
50.13
26.72
47.35
of our method, as demonstrated by the consistent improvements in
higher IoU thresholds.
Effectiveness of visual similarity. We estimate the distribution
parameters by considering both visual similarity and temporal
distance, as shown in Eq.(7). To evaluate the effectiveness of the
visual similarity, we set 𝜆1 = 0 and remove it from the calculation
of similarity between frames. The results are shown in Table 4,
where “v-s" represents the visual similarity. Comparing the results
in line 1 and 2 where the frame similarity computed by Eq.(7)
is directly used as the probability, and comparing the results in
line 6 and 8 where the Beta distribution is used for probability
calculation, we observe improvements of more than 2.5% and 2%
on 𝑅@1; 𝐼𝑜𝑈≥0.5, respectively, which clearly demonstrates the
importance of the visual similarity.
Effectiveness of intra-video loss. The intra-video loss, defined
in Eq.(17), enforces similarities between language queries and pos-
itive action candidates more than negative action candidates. It
is computed in a single language-video pair and helps to rank all
the action candidates, thus improving the accuracy of boundary
estimation. To evaluate the effectiveness of the intra-video loss, we
remove it for comparison, and the results are shown in Table 4. We
observe that compared with the result in line 1 without the intra-
video loss, the result in line 3 with the intra-video loss achieves
significant improvements of more than 4% on 𝑅@1; 𝐼𝑜𝑈≥0.7, and
about 2.5% on 𝑅@1; 𝐼𝑜𝑈≥0.5. This demonstrates that the intra-
video loss effectively improves the localization accuracy, especially
in high-precision scenarios. Similar trends of comparing the results
in line 5 and line 8, line 2 and line 7, and line 4 and line 6, further
verify the effectiveness of the intra-video loss.
Effectiveness of inter-video loss. The inter-video loss, defined
in Eq.(18), helps to leverage inter-sample information to learn the
diversities of actions and facilitate model training in the early stage.
However, it mainly focuses on learning the differences between dif-
ferent action instances rather than the fine-grained details of action
boundaries. To evaluate the effectiveness of the inter-video loss, we
remove it for comparison, and the results are shown in Table 4. We
observe that using the inter-video loss (lines 4, 5 and 8) improves
the accuracy by about 1% compared with the results without the
inter-video loss (lines 1, 2 and 7). However, this improvement is
relatively small compared to that of the intra-video loss, suggesting
that the intra-video loss is more effective in improving accuracy
and localization performance, especially in terms of high precision,
while the inter-video loss plays a complementary role in improving
the diversity of learned action representations.
4.5
Comparison with State-of-the-art Methods
We compare the proposed method with several state-of-the-art
methods at different levels of supervision, including fully-supervised
methods (CTRL [6], 2D-TAN [48], VSLNet [47]), weakly-supervised
methods (TGA [25], SCN [13], LoGAN [33], CRM [9]), and frame-
supervised methods (ViGA [4], LAS [42]).
The comparison results on the TACoS and Charades-STA datasets
are shown in Table 5 and Table 6, respectively. From the results,
we have observations as follows: (1) Compared with the frame-
supervised methods, i.e., ViGA [4], and LAS [42], our method
achieves more than 10% improvements on the 𝑅@1; 𝐼𝑜𝑈≥0.3
and 𝑅@1; 𝐼𝑜𝑈≥0.5 on the challenging TACoS dataset, and more
than 5% improvements on the 𝑅@1; 𝐼𝑜𝑈≥0.5 and 𝑅@1; 𝐼𝑜𝑈≥0.7
on the Charades-STA dataset, which demonstrates the effective-
ness of the proposed distribution-based method on modeling the
probabilities of pseudo-labels, especially in more difficult scenar-
ios; (2) Compared with the fully-supervised methods shown in the
upper parts of Table 5 and Table 6, our method achieves compara-
ble results, demonstrating the huge potential of the performance
of frame-supervised language-driven action localization; (3) Com-
pared with the weakly supervised methods shown in the middle
part of Table 6, our method outperforms all other methods in terms
of all metrics by a large margin, showing the superiority of our
method in the scenario that lacks full annotations. These results
suggest that our method achieves satisfying performance on the
TACoS and Charades-STA datasets and is a promising direction for
language-driven action localization.
4.6
Qualitative Analysis
We show several examples of action localization results of our
method and the baseline model on the Charades-STA dataset in
Figure 5. From the first three examples in Figure 5 (a), (b), and (c),
we observe that the action boundaries predicted by our method
are more accurate than the baseline model since the boundary
frames participate in training with appropriate probabilities in our
method. However, as shown in Figure 5 (d), both our method and
the baseline model fail to locate the action boundaries because
the video frames are too similar to distinguish, showing a lack of
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MM ’23, October 29–November 3, 2023, Ottawa, ON, Canada.
Shuo Yang, Zirui Shang, and Xinxiao Wu
Language query: a person kneeling on the floor talks on a phone
Ours(Beta)
Ground Truth
Baseline
0s
8.38s
0s
8.44s
1s
10.43s
3.30s
Beta Distribution
𝑃
ୠ
௙
𝑇
(a)
0
Language query: person throw it on the floor
Ours(Beta)
Ground Truth
Baseline
4.45s
9.85s
4.95s
8.94s
7.99s
𝑃
ୠ
௙
𝑇
Beta Distribution
4.39s
9.80s
0
(b)
(c)
0
Language query: a person puts food into a sandwich maker
Ours(Beta)
Ground Truth
Baseline
0s
9.09s
0s
9.20s
1s
10.18s
1.48s
Beta Distribution
𝑃
ୠ
௙
𝑇
Language query: lastly the person takes a drink from a cup
Ours(Beta)
Ground Truth
Baseline
12.23s
20.54s
1.96s
10.27s
14.65s
𝑃
ୠ
௙
𝑇
Beta Distribution
9.80s
15.70s
(d)
0
Figure 5: Examples of action localization results. “Baseline” denotes the results from the baseline model; “Ours (Beta)” denotes
the results predicted by the model using the Beta distribution; “Beta Distribution” denotes the curve generated by Eq.(14).
Table 5: Comparison with the state-of-the-art methods on
the TACoS dataset. Upper part: Fully-supervised methods;
Lower part: Frame-supervised methods.
Methods
𝑅@1; 𝐼𝑜𝑈≥𝜇
𝑚𝐼𝑜𝑈
0.3
0.5
0.7
CTRL [6]
18.32
13.3
-
-
TripNet [8]
23.95
19.17
-
-
ABLR [45]
19.50
9.40
-
-
DEBUG [21]
23.45
11.72
-
16.03
VSLNet [47]
29.61
24.27
20.03
24.11
2D-TAN [48]
37.29
25.32
-
-
ViGA [4]
19.62
8.85
3.22
15.47
LAS [42]
23.64
10.00
3.35
17.39
Ours (Beta)
36.14
20.17
7.30
23.09
sufficient fine-grained motion understanding. It is worth noting
that in all four examples in Figure 5, the estimated Beta distributions
are reasonable and provide guidance information according to the
annotated frame, thereby facilitating the cross-model alignment
and boundary estimation during training.
5
CONCLUSION
We have presented a novel probability distribution based method
for frame-supervised language-driven action localization. By using
distribution functions to model the probabilities of the action frame,
as well as the starting and ending boundaries of the target action,
our method is able to provide more accurate guidance in learning
cross-modal alignment and boundary estimation to compensate
Table 6: Comparison with the state-of-the-art methods on the
Charades-STA dataset. Upper part: Fully-supervised meth-
ods; Middle part: Weakly-supervised methods; Lower part:
Frame-supervised methods.
Methods
𝑅@1; 𝐼𝑜𝑈≥𝜇
𝑚𝐼𝑜𝑈
0.3
0.5
0.7
CTRL [6]
-
23.63
8.89
-
2D-TAN [48]
-
39.70
23.31
-
LGI [26]
72.96
59.46
35.48
51.38
VSLNet [47]
70.46
54.19
35.22
50.02
TGA [25]
32.14
19.94
8.84
-
SCN [13]
42.96
23.58
9.97
-
LoGAN [33]
51.67
34.68
14.54
-
CRM [9]
53.66
34.76
16.37
-
LAS [42]
60.40
39.22
20.17
39.77
ViGA [4]
71.21
45.05
20.27
44.57
Ours (Beta)
71.72
50.13
26.72
47.35
for the lack of supervision, thus successfully improving the accu-
racy of action localization. Extensive experimental results on two
benchmark datasets demonstrate the effectiveness of our method.
We believe that the distribution-based framework will be a promis-
ing direction for further research in the field of frame-supervised
language-driven action localization.
ACKNOWLEDGMENTS
This work was supported in part by the Natural Science Foundation
of China (NSFC) under Grant No 62072041.
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Explicit Granularity and Implicit Scale Correspondence Learning
for Point-Supervised Video Moment Localization
Anonymous Authors
ABSTRACT
Video moment localization (VML) aims to identify the temporal
boundary of the target moment semantically matching the given
query. Existing approaches fall into three paradigms: fully-supervised,
weakly-supervised, and point-supervised. Compared to other two
paradigms, point-supervised VML strikes a balance between lo-
calization accuracy and annotation cost. However, it is still in its
infancy due to the following two challenges: explicit granularity
alignment and implicit scale perception, especially when facing
complex cross-modal correspondences. To this end, we propose a
Semantic Granularity and Scale Correspondence Integration (SG-
SCI) framework aimed at modeling the semantic alignment between
video and text, leveraging limited single-frame annotation infor-
mation for correspondence learning. It explicitly models semantic
relations of different feature granularities and adaptively mines
the implicit semantic scale, thereby enhancing and utilizing modal
feature representations of varying granularities and scales. SG-SCI
employs a granularity correspondence alignment module to align
semantic information by leveraging latent prior knowledge. Then
we develop a scale correspondence learning strategy to identify and
address semantic scale differences. Extensive comparison experi-
ments, ablation studies, and necessary hyperparameter analyses on
benchmark datasets have demonstrated the promising performance
of our model over several state-of-the-art competitors.
CCS CONCEPTS
• Information systems →Novelty in information retrieval;
Multimedia and multimodal retrieval.
KEYWORDS
Cross-modal Moment Localization; Cross-Modal Retrieval; Corre-
spondence Learning
ACM Reference Format:
Anonymous Authors. 2024. Explicit Granularity and Implicit Scale Cor-
respondence Learning for Point-Supervised Video Moment Localization.
In Proceedings of the 32nd ACM International Conference on Multimedia
(MM’24), October 28-November 1, 2024, Melbourne, Australia. ACM, New
York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn
Unpublished working draft. Not for distribution.
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from permissions@acm.org.
ACM MM, 2024, Melbourne, Australia
© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-x-xxxx-xxxx-x/YY/MM
https://doi.org/10.1145/nnnnnnn.nnnnnnn
Query : A man poses for a picture in front of them
…
…
Video
Fully-supervised
Annotation:[98.31,103.34]
Point-supervised
Annotation:[101.16]
Weakly-supervised
Annotation:[-]
98.31
103.34
101.16
(a) Video Moment Localization
(b) Explicit Granularity Alignment and Implicit Scale Perception
…
…
GT
Fine-grained
Irrelevant
Query
Coarse-grained
Mismatch
…
…
Query1
Query2
Semantic
Inclusion
GT2
SF
PM
GT1
The man adds
pepper to the egg
The man is
preparing to cook
A woman is playing music, and a little boy walks by
Video
Figure 1: (a) Illustrative examples of Video Moment Localiza-
tion (VML). (b) Illustration of explicit granularity alignment
and implicit scale perception among VML. GT: Ground Truth;
PM: Prediction Moment; SF: Supervised Frame. (Left) Fine-
grained word-frame irrelevance in green and coarse-grained
sentence-moment mismatch in blue. (Right) Illustration of
the semantic inclusion relationships between textual and
visual correspondences.
1
INTRODUCTION
Video moment localization (VML), which refers to localizing a
visual moment corresponding to a given textual query, is a funda-
mental task in video understanding. It benefits many important
application scenarios, such as multimedia retrieval [45, 48] and
smart human–computer interaction [37, 43].
Current efforts [14, 44, 50, 53] focus on addressing both fully
and weakly supervised VML paradigms. As shown in Figure 1(a),
the fully-supervised method requires the ground-truth moment
annotations for training, which is laborious and time-consuming to
obtain [29]. The weakly-supervised method is more flexible because
it does not require moment annotations, but results in a significant
drop in performance [10]. To balance accuracy with annotation
cost, the point-supervised paradigm is proposed [21, 25]. Point-
supervised methods require a single frame annotation from the
ground truth, which is more practical and flexible compared to fully
supervised annotation, costing only 1/6 as much [24].
Prior research [2, 4, 18, 35], to enhance video-language compre-
hension, can be classified into two primary approaches: granularity
modeling and scale modeling. Granularity modeling involves delin-
eating relationships among various features (such as frame-word
and moment-sentence) through the explicit boundaries present in
video-language, facilitating the differentiation of data across vary-
ing entity granularities. In contrast, scale modeling derives from
the intricate semantic logics and inherent hierarchical structures
within textual and visual domains. This approach acknowledges

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Anonymous Authors
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implicit semantic scale in video and language interaction that
exists within an alternative modal space.
To be specific, as shown in Figure 1(b), existing point-supervised
labeling inevitably introduces two accompanied challenges: 1) Ex-
plicit Granularity Alignment. According to [10, 12], on the one
hand, single-frame annotations are randomized within intervals,
causing clear mismatches at a fine-grained level (frame-word); on
the other hand, the incomplete information on interval annotations
hinders the direct use of correspondences at a coarse-grained level
(moment-query). Undoubtedly, this challenge poses a significant
obstacle to effective language and visual alignment. 2) Implicit
Scale Perception. Prior studies [9, 16] note that query sentences
for the same video may vary in semantic scale. This issue is espe-
cially noticeable in the point-supervised approach. These methods
struggle to capture temporal relationships within a video because
of using only single-frame annotations, leading to a lack of effective
contextual modeling constraints. As a result, the essential semantic
scale information remains unmodeled, impeding temporal learning
and video comprehension.
To navigate these challenges, we introduce a novel Semantic
Granularity and Scale Correspondence Integration (SG-SCI) frame-
work that integrates a Granularity Correspondence Alignment (GCA)
module and a Scale Correspondence Learning (SCL) strategy. Firstly,
the GCA module is engineered to enhance the interaction of video-
language. By employing a fine-grained alignment approach, it es-
tablishes a more detailed and comprehensive mapping between
video frames and textual descriptions. This module not only facili-
tates a deeper understanding of the video content but also ensures
that even the less prominent frames find relevance in the corre-
sponding textual narrative. Secondly, the SCL strategy addresses
the disparity in temporal scale. It is designed to learn latent seman-
tics adaptively across varying temporal scales, enabling the model
to assimilate and correlate extensive video sequences with succinct
textual queries. This strategy ensures a more robust and contextu-
ally aware matching process, thereby enhancing the accuracy and
adaptability. Consequently, this framework offers a more nuanced
and adaptable solution for point-supervised VML.
To the best of our knowledge, it is the first work on integrating
explicit granularity alignment and implicit scale perception into
point-supervised VML. Our approach effectively mitigates the gran-
ularity and scale-related challenges by the synergy of the innovative
component and strategy. It boosts the precision of aligning video
moments to text queries and enhances the model’s robustness.
In summary, the main contributions are as follows:
• Model Contribution. We introduce an innovative Granular
Correspondence Alignment module. Specifically, it is designed
to improve the explicit correspondence relation across varying
granularities among different modalities.
• Strategy Contribution. Under the framework of point-supervised,
we develop a Scale Correspondence Learning strategy, which is
pivotal in capturing the implicit semantic scale in correspondence
learning.
• Experimental Contribution. Extensive experiments on two
benchmarks, i.e., Charades-STA [12] and TACoS [33], validate the
effectiveness and superiority of our model. The codes and settings
are released at https://anonymous.4open.science/r/SG-SCI.
2
RELATED WORK
2.1
Video-Language Modeling
Current research can be divided into two main categories depending
on the use of a modeling approach: feature granularity modeling
and semantic scale modeling. In terms of entities, the former is
explicit while the latter is implicit.
Early explicit modeling approaches [1, 12] mainly used global
sentence-level alignment to enhance the semantics of visual fea-
tures, but this approach overlooked fine-grained semantics such
as words. As a result, several studies [4, 35, 49] have investigated
various word-frame interactions using attention mechanisms to
capture the relationships between visual cues and textual queries.
Recent research [38, 47, 52] has recognized the significance of local
phrase patterns or tokens in sentences for video moment retrieval.
Compared to explicit modeling, implicit scale modeling [2, 11,
18, 19, 39] utilizes multi-scale relations to achieve better grounding
results. These relations mainly exist at the video-level and language-
level. However, all grounding methods overlook the possibility of
different semantic scales within the same level (e.g., sentence level).
Accordingly, DualMIL [9] extends multiple instance learning into a
two-level framework.
Although the aforementioned modeling strategies are effective,
most methods do not fully utilize their potential when dealing
with limited supervisory information. Therefore, our focus is on
extracting both implicit and explicit information to accurately locate
boundaries in point-supervised VML.
2.2
Video-Language Correspondence Learning
In the domain of video-language learning, the misalignment be-
tween textual descriptions and corresponding visual content intro-
duces significant challenges, necessitating the adoption of corre-
spondence learning as an innovative approach [15, 17, 20]. This
paradigm shift is epitomized by the introduction of MIL-NCE [31],
which pioneers the strategy of aligning video clips with adjacent
sentences to diminish the effects of misalignment. In contrast,
Tang et al. [40] improves video description quality by integrat-
ing an external image captioning model, emphasizing data-driven
enhancement over immediate error correction.
Our method differs itself from preceding endeavors through
two principal innovations. Firstly, we go beyond the traditional
focus on either model architectures or supervisory strategies by
enhancing improvements through the synergistic interaction be-
tween model and strategy learning. Secondly, we expand the use
of correspondence learning beyond segmented multi-modal data.
This pioneering adaptation allows for precise moment localization
with point-supervised for the first time.
2.3
Video Moment Localization
Different from existing fully and weakly-supervised VML stud-
ies [12, 14, 27, 30, 44, 50, 53], point-supervised VML strikes a deli-
cate balance between annotation efforts and model performance by
leveraging a single frame from the localization moment [10, 21, 25].
Compared to fully-supervised VML, point-supervised significantly
reduces the cost of annotation data and it provides more comprehen-
sive information than weakly supervised learning. By eliminating

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Explicit Granularity and Implicit Scale Correspondence Learning for Point-Supervised Video Moment Localization
ACM MM, 2024, Melbourne, Australia
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A little boy
holding a
yellow ball
walks by.
(a) Feature Extraction
Video
Encoder
Text
Encoder
(b) Multi-modal Interaction
Granularity Correspondence
Alignment
(c) Moment Localization
(d) Granularity Correspondence Alignment
(e) Scale Correspondence Learning
Training
Inferencing
Moment to Frame
Frame to Moment
1
2
3
4
T
…
Positional
Embedding
Self-Attention Block
Self-Attention Block
boy
yellow
ball
walks
Cross-Attention Block
…
Step 1
Step 2
…
Pooling
Temporal Weight
Semantic Score
Gaussian
Distribution
…
p ∼N
!
µf, σ2
f
"
Granularity Perception Matrix
yellow
boy
walks
ball
𝑠!! 𝑠!"
𝑠#!
𝑠#"
𝑠$! 𝑠$"
𝑠!%
𝑠#%
𝑠$%
…
…
…
…
…
…
…
…
…
…
…
1
2
𝑇
…
A little boy
holding a
yellow ball
walks by.
yellow
boy
walks
ball
𝑇
Sentence-Frame
Adaptive Granularity Balancing
…
Mean Weighted
×𝛼
×(1 −𝛼)
Output
Input
Granularity Aggregation
Global Perception
…
…
…
…
…
𝑚!
"
𝑚!#$
"
𝑚%
#
𝑚%#$
#
0
0
0
1
1
…
…
…
𝑙!
𝑙"
𝑙"#!
𝑙$#!
𝑙$
p ∼N
!
µf, σ2
f
"
0
0
0
𝜅$
𝜅$#!
…
…
…
Label
KL Divergence
…
…
…
𝑚!
"
𝑚!#$
"
𝑚%
#
𝑚%#$
#
KL Divergence
element dot product
matrix multiplication
element addition
element average
…
…
…
1
2
3
4
T
…
𝒂𝒗𝒈
𝒂𝒗𝒈
𝑭
⋅
⋅
⋅
…
3
𝑇−1
⋅
Figure 2: Schematic illustration of the proposed SG-SCI model. We first perform (a) Feature Extraction on both video and
language separately, followed by (b) Multi-modal Interaction, which combines the obtained cross-attention features and
similarity information, feeding them into the (d) Granularity Correspondence Alignment module. After the interaction, the
features are input into the (c) Moment Localization module. During the training phase, we apply a (e) Scale Correspondence
Learning strategy to compare semantics at diverse scales.
the need for precise start and end timestamps for the target moment,
a quick "glance" at the video and the selection of a single frame are
often sufficient, making point-supervised methods applicable to sce-
narios with incomplete annotation information [46]. The concept
of point-supervised was initially introduced by Bearman et al. [3]
in the context of semantic segmentation tasks and Cui et al. [10]
applied the concept of point-supervised to VML and introduced the
ViGA model, which aligns Gaussian distributions generated from
supervised frame positions with cross-modal attention. Subsequent
works have built upon ViGA’s foundation, D3G [25] is proposed
to align features of sentence-moment pairs and dynamically miti-
gate annotation bias using Gaussian distributions, while CFMR [21]
develops a concept-driven multi-modal alignment mechanism to
circumvent the need for cross-modal interaction modules during
the inference process.
Despite efforts to enhance retrieval performance, existing work
still falls short in effectively modeling the semantic granularity
relationship between two modalities. Furthermore, due to the con-
straints of the supervised information, it struggles to capture the
overall action changes within a moment.
3
METHODOLOGY
This section commences by defining the problem and outlining the
core pipeline of point-supervised video moment localization. After
this, we will explore the insights behind our innovative Granularity
Correspondence Alignment method, highlighting its main elements.
Finally, we conclude with an exposition of our Scale Correspondence
Learning strategy, showing its significance in our research. Figure 2
presents an overview of our proposed SG-SCI.
3.1
Problem Definition
In this work, we aim to address the challenge of cross-modal video
localization under point-supervised. The dataset comprises a series
of quadruples and is denoted as D, which is expressed as follows,
D = {(V
𝑖, Q𝑖,𝜏𝑠
𝑖,𝜏𝑒
𝑖) | 𝑖= 1 to 𝑁},
(1)
where V
𝑖represents an untrimmed video, Q𝑖denotes the associ-
ated query sentence, and 𝜏𝑠
𝑖, 𝜏𝑒
𝑖are the start and end timestamps
respectively. The target video moment aligns with the query Q𝑖.
Contrasting with a fully-supervised paradigm, our training is
based on a collection of triplet annotations, symbolized as A =
{(V
𝑖, Q𝑖,𝜏𝑚
𝑖) | 𝑖= 1 to 𝑁}, where 𝜏𝑚
𝑖
signifies a randomly selected
point within the interval defined by 𝜏𝑠
𝑖and 𝜏𝑒
𝑖. During the infer-
ence phase, the objective is to precisely localize the relevant video
moment for each query within the dataset D.
3.2
Pipeline of Point-supervised VML
To fulfil the task of Point-supervised VML, we first introduce the
pipeline which can be categorized into three parts: Featurn Extrac-
tion, Multi-modal Interaction, and Moment Localization.
3.2.1
Feature Extraction. Before multi-modal interaction, we ex-
tract the visual and textual features, and then incorporate learnable
positional embedding into the extracted features.
Visual Representation. Given an untrimmed video V, our
approach utilizes a pre-trained CNN, i.e., C3D [41] or I3D [6], to
extract visual features to ensure fair comparison. The extracted
features are then processed through a fully-connected (FC) layer,
obtaining a visual representation denoted as 𝑉= {𝑣1, 𝑣2, . . . , 𝑣𝑇},

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where 𝑇represents the number of frames sampled at intervals in
the video and 𝑣𝑖denotes the 𝑖-th frame.
To perceive the time of the video, we incorporate learnable Po-
sitional Embedding (PE) into the model and get enhanced visual
representation ¯
𝑉∈R𝑇×𝑑, where 𝑑represents the dimension of the
visual representation.
Text Representation. For a given query Q, we employ the pre-
trained Glove model [32] to extract textual features. A bi-directional
gated recurrent unit (Bi-GRU) is employed to obtain the latent se-
quential order of original sentence, and get new textual representa-
tion 𝑄= {𝑞1,𝑞2, . . . ,𝑞𝐿}, where 𝐿indicating the number of words
in the query. Similar to the visual representation, PE is also added to
the text representation, obtaining the enhanced features ¯
𝑄∈R𝐿×𝑑.
3.2.2
Multi-modal Interaction. To capture the intra-model seman-
tics, we utilize ¯
𝑉and ¯
𝑄through the self-attention layer [42] to
generate the intra-modal representation ˆ
𝑉and ˆ
𝑄. Similarly, we use
the cross-modal multi-head attention to obtain the cross-modal
representation ˜
𝑉= 𝐴𝑡𝑡𝑛( ˆ
𝑄, ˆ
𝑉) and ˜
𝑄= 𝐴𝑡𝑡𝑛( ˆ
𝑉, ˆ
𝑄), where 𝐴𝑡𝑡𝑛(·)
is formulated as follows,
𝐴𝑡𝑡𝑛( ˆ
𝑄, ˆ
𝑉) = softmax(
ˆ
𝑄ˆ
𝑉𝑇
√
𝑑
) ˆ
𝑉,
𝐴𝑡𝑡𝑛( ˆ
𝑉, ˆ
𝑄) = softmax(
ˆ
𝑉ˆ
𝑄𝑇
√
𝑑
) ˆ
𝑄.
(2)
Subsequently, to serve the subsequent modules, we take out
the last layer of cross-modal attention scores as the cross-modal
attention scores 𝐴𝑞→𝑣and 𝐴𝑣→𝑞, which will be further illustrated
in 3.3. We input the features and attention scores into our proposed
granularity correspondence alignment module to obtain the multi-
granularity representation ˜
𝑉𝑓and ˜
𝑄𝑤:
˜
𝑉𝑓= GCA( ˜
𝑉, ˜
𝑄,𝐴𝑞→𝑣),
˜
𝑄𝑤= GCA( ˜
𝑄, ˜
𝑉,𝐴𝑣→𝑞),
(3)
where GCA(·) is the granularity correspondence alignment mod-
ule. So far, we have obtained the multi-granularity representa-
tion ˜
𝑉𝑓and ˜
𝑄𝑤via multi-modal interaction. The representation is
compound of multi-level granularities information, assisting cross-
modal semantic alignment. In order to integrate the semantic in-
formation of the whole sentence, we use the max-pooling function
F(·) to obtain the sentence representation ˜
𝑄𝑠as follows,
˜
𝑄𝑠= F( ˜
𝑄𝑤).
(4)
3.2.3
Moment Localization. Moment localization aims to locate
the start and end timestamps based on the obtained representation.
In the training stage, unlike fully-supervised scenario, a random
point within the target video moment is accessed while it is absent
during inference. Because of the divergence between the two stages,
we will elaborate on both of them subsequently.
Training Stage. In the training process, we use sliding windows
to slice ˜
𝑉𝑓, and generate candidate moments. Gaussian distribution
is utilized to measure the moments’ temporal features and cosine
similarity of semantic features. The temporal information is defined
as follows,
𝐺𝑖=
1
√
2𝜋𝜎
exp
©

«
−

(𝑖−𝜏𝑚) ·
2
𝐿𝑣−1
2
2𝜎2
ª
®
®
¬
,
(5)
where 𝑖is the 𝑖-th frame, 𝜏𝑚is the index of the supervised frame, 𝜎
is a hyperparameter, and 𝐿𝑣is the length of the video. The semantic
information 𝑆𝑖is the cosine similarity between ˜
𝑉𝑓(𝑖) and ˜
𝑄𝑠.
These temporal and semantic analyses are integrated and applied
in our scale correspondence learning strategy, detailed in Sec. 3.4.
Inference Stage. The inference involves two primary steps,
consisting of identifying the key point that best matches query and
expand from this point to get the predicted video moment most
similar to the query.
3.3
Granularity Correspondence Alignment
In this section, we introduce a novel granularity correspondence
alignment module within the framework of point-supervised VML,
which is designed to adaptively regulate the granularity relation-
ship between different modalities. By exploring semantic relation-
ships across modalities, the module enriches the original modal
representation and supplements existing semantic alignment with
information across various granularities, ensuring symmetry in
intermodal interactions.
Unlike existing methods that unconsciously interact the small-
est granularity units (frames and words) in representations across
modalities, our main insights derive from exploiting the attention-
perceived matrix and utilizing it to capture potential cross-modal
semantic granularity information. Specifically, we use the matrix
consisting of cross-modal attention scores as a granularity percep-
tion matrix as follows,
𝐴𝑞→𝑣=










𝑠11
𝑠12
· · ·
𝑠1𝐿
· · ·
· · ·
· · ·
· · ·
𝑠𝑖1
𝑠𝑖2
· · ·
𝑠𝑖𝐿
· · ·
· · ·
· · ·
· · ·
𝑠𝑇1
𝑠𝑇2
· · ·
𝑠𝑇𝐿










,
(6)
where 𝐴𝑞→𝑣∈R𝑇×𝐿is the cross-modal attention score matrix, 𝑇
and 𝐿are the number of frames and words, respectively, and 𝑠𝑖𝑗is
the attention score between the 𝑖-th frame and the 𝑗-th word. The
differences of scores in each column represent how much attention
the word pays to the video frame. Therefore, we consider using
max function to aggregate the information in each row to obtain
the potential prior distribution ˜
𝑉𝑝∈R𝑇of the complete query with
respect to the video frames, formulated as,
˜
𝑉𝑝= max(𝐴𝑞→𝑣) = [max(𝑠1·), max(𝑠2·), . . . , max(𝑠𝑇·)]𝑇.
(7)
Next, we calculate the cosine similarity between the query and
the original video frames to obtain the prior distribution of the
complete query with respect to the video frames,
˜
𝑉𝑔= 𝑐𝑜𝑠( ˜
𝑉, ˜
𝑄𝑠),
(8)
where 𝑐𝑜𝑠(·) is the cosine similarity function. Subsequently, we use
the finest granularity information from attentional perception and
pooled global granularity information, which are averaged and used
as adaptive visual granularity perceptual features ˜
𝑉𝑐= 1
2 ( ˜
𝑉𝑔+ ˜
𝑉𝑝),
In this way, we can obtain the visual granularity perception vector

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˜
𝑉𝑐, which can be used to enhance the original visual representation.
On this basis, we use the obtained features to remodel the cross-
modal fusion with text feature,
˜
𝑉𝑎= ˜
𝑉𝑐⊙˜
𝑉𝑓.
(9)
After reaggregation, we integrate self-attention-based fine-grained
features with multi-scale visual perception features. This enhances
fine-grained entity alignment and focuses on overall perception,
thereby improving representation. We then apply these features for
cross-modal semantic interactions, using prior semantic knowledge
to refine visual-word correlations.
Finally, we use the weighted mean approach in order to control
the original modal feature information, thus preventing noise inter-
ference due to excessive introduction of cross-modal information
as follows,
˜
𝑉𝑓= 𝛼˜
𝑉+ (1 −𝛼) ˜
𝑉𝑎,
(10)
where 𝛼is the mean weighted factor. We use the same strategy
applied to another modality. Therefore, we can obtain the final cross-
modal representation ˜
𝑉𝑓and ˜
𝑄𝑤via the multi-modal interaction.
3.4
Scale Correspondence Learning
To improve the model’s understanding ability under different scales
of moments, we utilize potential frame-moment correspondence
semantic information, as the single-frame supervised information
alone is not sufficient for effective moment semantics. To be specific,
the target of optimization consists of three parts, the first part
models the semantic and temporal information of the fused features
from a global perspective. The second part uses point annotation
to compare the differences between potential positive and negative
samples in different intervals to effectively capture the information
about the change of actions within the intervals. The third part
aims to exploit the single point information in the global moment
that is most similar to the query, which in turn enriches the priori
knowledge and enhances the guidance of cross-modal semantics.
3.4.1
Global Alignment Loss. We take the cross-entropy loss to
force the representation information we get to be close to the in-
formation provided by the supervised frames in terms of temporal
distance and semantic distance,
𝐿𝑔= −
𝑇
∑︁
𝑖=1
𝐺𝑖log𝑆𝑖,
(11)
where 𝐺𝑖is the Gaussian distribution weight of the 𝑖-th frame, and
𝑆𝑖is the semantic similarity score of the 𝑖-th frame.
3.4.2
Frame-moment Correspondence Loss. Due to lacking of bound-
ary annotations, a single point’s information inadequately repre-
sents the interval matching the query. This makes us to consider
using video moments that encompass point annotation as potential
positives. We construct binary labels to facilitate interval percep-
tion: moments containing the point annotation are labeled 1, while
others are labeled 0, as follows,
y𝑖=
(
1,
𝜏𝑚∈M𝑖,
0,
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒,
(12)
where M𝑖is the 𝑖-th moment by sliding window. Due to the latent
differences in temporal information of these moments, we introduce
Gaussian distribution weights to further construct the soft labels
˜
y𝑖, which can be formulated as:
˜
y𝑖= y𝑖W(M𝑖),
(13)
where W(·) = 𝐺(𝑠) ∗𝐺(𝑒), 𝐺(𝑠) and 𝐺(𝑒) are the Gaussian distri-
bution weights at the beginning and end of M𝑖, respectively. Since
it is inevitable that there will be multiple positive sample moments
belonging to a query, it is inappropriate to view similarity score
learning as a 1-in-N classification problem with cross-entropy loss.
To this end, we utilize the Kullback-Leibler divergence to construct
frame-moment correspondence loss 𝐿𝑓→𝑚
𝑐
, as follows,
𝐿𝑓→𝑚
𝑐
=
𝑁
∑︁
𝑖=1
˜
y𝑖log ˜
y𝑖
S𝑖
,
(14)
where S𝑖is the cosine similarity calculated by moment M𝑖and
query feature ˜
𝑄𝑠.
3.4.3
Moment-frame Correspondence Loss. In the beginning of lo-
cation process, we focus on identifying a point that align most
closely with the query, enabling our model to discern and empha-
size subtle points’ differences. We separate essential information
from positive sample moments, concentrating on feature variances.
Specifically, we extract detailed point data reflecting semantic labels
across moments, leveraging this to enrich point annotation with
inherent disparities.
This process can be represented as follows,
K𝑖= ˜
𝑉𝑓(𝜃),𝜃= arg max
𝑗∈[𝑠,𝑒] 𝑆𝑗,
(15)
where 𝑠and 𝑒are the start and end index of the moment M𝑖.
For label construction, we use both hard sample labels and soft
labels with Gaussian weights, just as the Eqn. (12) and (13). We
then employ KL divergence to analyze differences between positive
and negative samples, using these insights to refine our alignment
process, as follows,
𝐿𝑚→𝑓
𝑐
=
𝑁
∑︁
𝑖=1
˜
y𝑖log ˜
y𝑖
K𝑖
.
(16)
By combining the global loss 𝐿𝑔and correspondence loss 𝐿𝑐, we
obtain the final loss for model optimization, as follows,
𝐿= 𝐿𝑔+ 𝛽𝐿𝑓→𝑚
𝑐
+ 𝛾𝐿𝑚→𝑓
𝑐
,
(17)
where 𝛽and 𝛾are used to balance the focus between moments and
frames, avoiding the model over underscore the sample information.
4
EXPERIMENTS
This section will begin by presenting the experimental settings.
Subsequently, we will conduct comparative experiments, compre-
hensive ablation studies, and evaluate the effectiveness of SG-SCI
to answer the following three research questions (RQs):
• RQ1: Is our proposed SG-SCI able to outperform several state-
of-the-art competitors on VML?
• RQ2: Is each component of our SG-SCI helpful for boosting the
localization performance?
• RQ3: How do hyperparameters affect model capability?

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4.1
Datasets
The experiments that follow are conducted on two benchmark
datasets: Charades-STA [12] and TACoS [33].
Charades-STA [12]: It is constructed on the Charades dataset [36]
and contains 16,128 "moment-query" pairs with an average video
duration of 30 seconds. Following the standard split strategy [12],
we divided the dataset into 12,408 and 3,720 "moment-query" pairs
for training and testing, respectively.
TACoS [33]: It is built upon MPII Cooking Compositive dataset [34]
and only covers cooking activities that contain pairs of queries with
very similar visual information. It consists of 127 untrimmed videos
with the average duration of 320 seconds and 18,818 queries.
In particular, we use point-supervised information instead of
boundary supervision information in the original dataset setting,
just like existing effort [10], where the point-supervised information
comes from a random point in the boundary.
4.2
Experimental Settings
4.2.1
Evaluation Metrics. Following the existing work [10, 21, 25],
we choose 𝑅𝑛@𝑚and mean averaged IoU (mIoU) as protocols to
evaluate the performance of moment localization. To be specific,
𝑅𝑛@𝑚refers to the percentage of predictions for which the tempo-
ral Intersection over Union (IoU) surpasses the thresholds 𝑚within
the top-𝑛of the sorted results, and mean averaged IoU (mIoU) rep-
resents the average IoU across all test samples. Since our method
localizes the target moment with the highest coordinate probability,
we set 𝑛= 1 and 𝑚∈{0.3, 0.5, 0.7}. The higher 𝑅𝑛@𝑚and mIoU
are better.
4.2.2
Implementation Details. In our work, we employ the pre-
trained I3D [6] and C3D [41] network to extract visual features
from Charades-STA [12] and TACoS [33] respectively. Consistent
with prior research [10], we keep the word embedding module
fixed and utilize the 840B GloVe [32] to construct a comprehensive
vocabulary. The model dimension 𝑑𝑚𝑜𝑑𝑒𝑙is set to 512, and we
employ AdamW [28] with a learning rate of 1𝑒−4, which decays by
half when reaching a plateau during training. By default, we set
𝛼= 0.3, 𝛽= 𝛾= 0.1, and 𝜎= 0.04 in both datasets. The batch sizes
for the Charades-STA and TACoS are empirically set to 512 and 64,
respectively. All experiments are conducted on a NVIDIA GeForce
RTX 4090 with 24GB memory.
4.3
Performance Comparison (RQ1)
In this subsection, we compare the proposed method with different
kinds of state-of-the-art methods, including fully-supervised meth-
ods (2D-TAN [51], SS [11], FVMR [13], ADPN [7], MS-DETR [22]),
weakly-supervised methods (SCN [26], CNM [54], CWG [8], CPL [55],
IRON [5], PPS [23]) and point-supervised methods (ViGA [10],
PSVTG [46], CFMR [21], D3G [25]). To ensure the validity of the
comparison, the majority of the selected methods are drawn from
the last three years. The comparisons on Charades-STA and TACoS
are encapsulated in Table 1 and Table 2 respectively. Based on the
data presented, the following observations can be derived:
• Our method mines more correspondence information com-
pared with existing point-supervised methods. While tra-
ditional point-supervised methods focus on the interaction of
Table 1: Performance comparison on Charades-STA with
different supervision methods. Bold means the best result in
point-supervised method and underline means the second
best.
Type
Method
R1@0.3 R1@0.5 R1@0.7
mIoU
Fully-supervised
2D-TAN [AAAI20] [51]
-
50.62
28.71
-
SS [ICCV21] [11]
-
56.97
32.74
-
FVMR [ICCV21] [13]
-
55.01
33.74
-
ADPN [ACMMM23] [7]
70.35
55.32
37.47
51.15
Weakly-supervised
CWG [AAAI22] [8]
43.41
31.02
16.53
-
CPL [CVPR22] [55]
66.40
49.24
22.39
-
IRON [CVPR23] [5]
70.28
51.33
24.31
-
PPS [AAAI24] [23]
69.06
51.49
26.16
-
Point-supervised
ViGA [SIGIR22] [10]
71.21
45.05
20.27
44.57
PSVTG [TMM22] [46]
60.40
39.22
20.17
39.77
CFMR [ACMMM23] [21]
-
48.14
22.58
-
D3G [CVPR23] [25]
-
43.82
20.46
-
SG-SCI (Ours)
70.30
52.07
27.23
46.77
Table 2: Performance comparison on TACoS with different
supervision methods. Bold means the best result in point-
supervised method and underline means the second best.
Type
Method
R1@0.3 R1@0.5 R1@0.7
mIoU
Fully-supervised
2D-TAN [AAAI20] [51]
37.29
25.32
-
-
SS [ICCV21] [11]
41.33
29.56
-
-
FVMR [ICCV21] [13]
41.48
29.12
-
-
MS-DETR [ACL23] [22]
47.66
37.36
25.81
35.09
Weakly-supervised
SCN [AAAI20] [26]
11.72
4.75
-
-
CNM [AAAI22] [54]
7.20
2.20
-
-
CPL [CVPR22] [55]
11.42
4.12
-
-
Point-supervised
ViGA [SIGIR22] [10]
19.62
8.85
3.22
15.47
PSVTG [TMM22] [46]
23.64
10.00
3.35
17.39
CFMR [ACMMM23] [21]
25.44
12.82
-
-
D3G [CVPR23] [25]
27.27
12.67
4.70
-
SG-SCI (Ours)
37.47
20.59
8.27
23.83
sentence-moments [25], our method involves multi-granularity
interactions among complex video, concise textual descriptions,
and point annotation. Therefore, the model can consider the in-
teraction between sentences and point annotation at multiple
granularities during the training process, resulting in better cross-
modal information interaction. As illustrated in Table 1, in the
Charades-STA dataset, SG-SCI achieves 4.65% and 6.77% higher
than CFMR [21] and D3G [25] in 𝑅1@0.7, respectively.
• SG-SCI reduces the performance disparity between fully-
supervised methods and point-supervised methods. Lack-
ing of annotated timestamps, the positioning accuracy of point-
supervised methods is lower compared to existing supervised
learning methods. By learning features across different scales, SG-
SCI exploits the information contained in the supervised frames,
to some extent compensating for the performance gap caused by
different supervised information. In the Charades-STA dataset,
our method even outperforms 2D-TAN [51], fully-supervised
method, by 1.45% in terms of 𝑅1@0.5.
• Trivial annotation cost contributes to localization. The ex-
istence of frame annotation provides the model with a clear
direction during iterative convergence, and our proposed corre-
spondence module enables the model to continuously converge
during gradient backpropagation based on the multi-granularity
information from query and frame annotation. As evidenced in
Table 1 and Table 2, our approach consistently outperforms the
cutting-edge weakly-supervised models.

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(a) Component Ablation.
(b) GCA Ablation.
(c) Mean Weighted Factor.
Figure 3: Impact of (a) GCA and SCL, (b) multiple granularities in GCA, and (c) the mean weighted factor 𝛼in GCA. Evaluation
performed on Charades-STA.
Table 3: Comparison of different loss ablation for our frame-
work. "means retaining it and %means removing it.
Global F-M M-F R1@0.3 R1@0.5 R1@0.7 mIoU
"
%
%
66.83
37.74
14.14
40.81
%
"
%
50.56
27.96
10.27
31.04
%
%
"
41.13
23.87
9.46
26.33
%
"
"
47.50
25.59
9.70
29.13
"
%
"
70.00
44.70
19.81
44.04
"
"
%
69.89
42.07
17.50
43.28
"
"
"
70.30
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27.23
46.77
• SG-SCI can handle more challenging task. In contrast to
Charades-STA, TACoS contains longer videos with shorter re-
trieved moments, and each frame within the video exhibits a high
degree of semantic approximation, demanding a heightened abil-
ity from the model to perceive boundaries and differentiate simi-
lar moments. Consequently, existing weakly-supervised methods
may struggle to address this scenario, resulting in a significant
absence of the 𝑅1@0.7 metric in Table 2. However, compared to
existing point-supervised methods, like ViGA [10] and D3G [25],
our approach exhibits great performance in 𝑅1@0.7.
4.4
In-depth Analysis (RQ2 & RQ3)
To demonstrate each component of SG-SCI, we conducted extensive
ablation experiments on Charades-STA.
4.4.1
Ablation Study.
• Impact of GCA and SCL. To discuss whether the proposed
GCA and SCL can achieve better performance, we simplify the
pipeline of SG-SCI and set it as the baseline. Specifically, after
multi-modal interaction, we train the model by calculating the
KL divergence of the similarity distribution between the query
and each frame with a Gaussian distribution. To demonstrate the
effectiveness of SCL, we conduct experiments by setting the soft
labels to 1. From the Figure 3(a), it can be seen that both GCA and
SCL effectively enhance the localization ability of the baseline,
especially in 𝑅1@0.7.
• Effect of multiple granularities in GCA. As described in
Sec 3.3, GCA not only helps to understand the video contents
more deeply but also ensures that less significant frames have
relevance in the corresponding textual narrative. We validate
the effectiveness of multi-granularity information interaction
on cross-modal representation, with specific results shown in
Figure 3(b). It can be seen that the multi-granularity interaction
proposed in GCA has effectively improved the localization effect
by enhancing single-modal representation. At the same time,
increasing the multi-granularity interaction of queries can en-
hance the model’s ability to perceive fine-grained boundaries,
i.e., improve the 𝑅1@0.7 by 6.10%.
• Effectiveness of different loss. The final loss contains three
parts: global loss, frame-moment correspondence loss and moment-
frame correspondence loss. To evaluate the effectiveness of them,
we remove some of them for comparison and the results are
shown in Table 3, and we find that scale correspondence learning
can inspire the potential of the model for boundary perception.
In the presence of global loss, the frame-moment correspondence
loss and moment-frame correspondence loss alone can enhance
performance. The combination of the two can produce compre-
hensive performance improvement, and the 𝑅1@0.7 even gets
more than 10% improvement than only using global loss, showing
that scale correspondence learning can enhance the ability to
capture key information and boundary perception.
4.4.2
Parameter sensitivity.
• The mean weighted factor 𝛼in GCA. An important hyperpa-
rameter in GCA is 𝛼, which is used when combining the different
granularities. It represents how much information is interacted
and 𝛼= 1 represents merely using global feature information
while 𝛼= 0 denotes only utilizing the fine granularity infor-
mation. The specific results are shown in Figure 3(c). It can be
observed that each metric shows a trend of first rising and then
falling. Moreover, different difficulty of tasks represent diverse 𝛼:
for coarse-grained localization task (𝑚= 0.3), 𝛼= 0.5 is suitable
while for fine-grained localization task, i.e., 𝑚= 0.5 and 𝑚= 0.7,
𝛼= 0.3 is better.
• The importance of frame-moment correspondence loss
and moment-frame correspondence loss. In the Eqn. (17),
𝛽and 𝛾represent the importance of frame-moment correspon-
dence loss and moment-frame correspondence loss in the final
loss. We test different combinations of 𝛽and 𝛾in the range

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Figure 4: The results of R1@0.7 from different frame-
moment correspondence loss and moment-frame correspon-
dence loss. Evaluation performed on the Charades-STA
dataset.
Query: She picks up the rinds and throws them away.
Ground Truth
90.2 s
95.2 s
VIGA
Ours
86.4 s
98.1 s
96.4 s
88.7 s
Figure 5: Visualization of localization results on TACoS. The
yellow bar represents the ground truth temporal boundaries
of the language query, the blue bar depicts the predicted
boundaries of ViGA, and the green bar signifies the predicted
boundaries of SG-SCI.
{0.05, 0.1, 0.2, 0.5, 1}, and display the numerical relationship be-
tween 𝑅1@0.7, as shown in Figure 4. Similar to the 𝛼in GCA,
𝛽and 𝛾also show an overall trend of first increasing and then
decreasing, and the highest value is obtained at 𝛽= 𝛾= 0.1. The
experimental results indicate that frame-moment correspondence
loss and moment-frame correspondence loss have a significant
impact on fine-grained boundary perception.
• Sliding Window Size. Sliding window is designed to capture
multi-granularity features via multi-scale sliding windows. In
fact, monotonically increasing or decreasing the size of the slid-
ing window would lead to degradation of the representation
discrimination, thus affecting overall performance. For instance,
in extreme cases, the sliding window may encompass information
from either the entire video or just a frame. In such scenarios the
model struggles to learn and comprehend these moments holisti-
cally, leading to a decline in overall localization performance. We
conducted ablation experiments on the Charades-STA dataset,
with a default step size of 4. The specific experimental results are
shown in Table 4. The results indicate that the best performance
is achieved when the sliding window size is 8, indicating that
the model needs a moderate sliding window size for sufficient
learning during Scale Correspondence Learning.
Table 4: Performance comparison on Charades-STA with
different sliding window sizes, and ∗means the stride is the
half of sliding window size.
Size
R1@0.3 R1@0.5 R1@0.7 mIoU
4
70.89
49.46
23.66
46.02
8
70.30
52.07
27.23
46.77
16
70.78
47.18
21.99
45.23
24
70.43
46.02
21.48
44.96
32
69.84
45.3
19.46
44.22
4∗
70.86
49.46
23.31
45.91
16∗
70.83
47.15
22.07
45.22
24∗
70.46
45.86
21.64
44.70
32∗
69.09
44.44
20.16
44.03
Table 5: Speed comparison on Charades-STA and TACoS be-
tween ViGA [10] and SG-SCI, time is averaged.
Charades-STA
TACoS
Method
Train Inference
Train Inference
ViGA [10]
1.0x
1.0x
1.0x
1.0x
SG-SCI
1.1x
1.2x
1.0x
1.2x
4.4.3
Inference speed. As Table 5 illustrated, during the training
and inference phases, our runtime is almost identical to ViGA’s [10].
This is because our proposed GCA and SCL do not introduce sig-
nificant computational complexity. As a result, we consider our
methods to be competitive in terms of efficiency.
4.4.4
Quantitative Results. To more thoroughly examine the contri-
butions of our proposed SG-SCI framework, we present an example
illustrating the results of moment retrieval on the TACoS datasets.
As is shown from the Figure 5, SG-SCI demonstrates superior effec-
tiveness in terms of boundary perception. This can be attributed
to the training process, where our model effectively enhances the
model’s perceptual ability across different scales.
5
CONCLUSION AND FUTURE WORK
In this paper, we designed a new framework to understand the cor-
respondence connections between video and text. This framework
used a limited amount of data from single frame to learn. Initially,
we looked at the problems existing methods had in linking the mean-
ing of video and text that varied in detail. To address these issues,
our method included two main strategies: (1) creating a model that
used underlying knowledge to connect features of different modal-
ities, and (2) developing a learning strategy that focused on the
differences in information between similar samples. These strate-
gies helped our method to clearly define and find the differences
in meaning across various details and modalities. This made our
method better at representing features of explicit granularity and
implicit scales. Experiments conducted on two benchmark datasets
demonstrated the effectiveness of our proposed framework.
In the future, in order to further improve the performance of
VML, we plan to introduce a more advanced visual-language trans-
former backbone. In addition, inspired by the fully-supervised par-
adigm, we plan to study the impact of different inference methods
on localization performance.

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IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023
6121
Point-Supervised Video Temporal Grounding
Zhe Xu
, Kun Wei
, Xu Yang, Member, IEEE, and Cheng Deng
, Senior Member, IEEE
Abstract—Given an untrimmed video and a language query,
Video Temporal Grounding (VTG) aims to locate the time interval
in the video semantically relevant to the query. Existing fully-
supervised VTG methods require accurate annotations of temporal
boundary, which is time-consuming and expensive to obtain. On
the other hand, weakly-supervised VTG methods where only
paired videos and queries are available during training lag far
behind the fully-supervised ones. In this paper, we introduce
point supervision to narrow the performance gap with affordable
annotating cost and propose a novel method dubbed Point-
Supervised Video Temporal Grounding (PS-VTG). Speciﬁcally,
an attention-based grounding network is ﬁrst employed to obtain
a language activation sequence (LAS). Then pseudo segment-
level label is generated based on the LAS and the given point
supervision to assist the training process. In addition, multi-
level distribution calibration and cross-modal contrast are framed
to obtain discriminative feature representations and precisely
highlight the language-relevant video segments. Experiments on
three benchmarks demonstrate that our method trained with
point supervision can signiﬁcantly outperform weakly-supervised
approaches and achieve comparable performance with fully-
supervised ones.
Index Terms—Cross-modal contrast, multi-level distribution
calibration, point supervision, video temporal grounding.
I. INTRODUCTION
W
ITH the explosive growth of video data, video under-
standing has attracted increasing research attention in
the past few years. Traditional video understanding tasks, e.g.,
video classiﬁcation [1], [2], object tracking [3], [4], and action
localization [5], [6] are limited in the video modality and pre-
deﬁned action categories. Recently, Video Temporal Grounding
(VTG) [7], [8], [9] was introduced to locate the time interval in
an untrimmed video semantically relevant to a language query,
which involves both the video and language modalities. Due to
the requirement of the comprehensive understanding and pre-
cise alignment of the multi-modal information, VTG is a more
challenging and practical task.
According to the supervision available during training, exist-
ing VTG methods can be divided into two categories: fully-
supervised VTG [7], [8], [10], [11] and weakly-supervised
Manuscript received 21 May 2022; revised 7 August 2022 and 1 Septem-
ber 2022; accepted 3 September 2022. Date of publication 9 September 2022;
date of current version 1 November 2023. This work was supported in part
by the National Natural Science Foundation of China under Grants 62132016,
62171343, and 62071361, in part by the Key Research and Development Pro-
gramofShaanxiunderGrant2021ZDLGY01-03,andinpartbytheFundamental
Research Funds for the Central Universities under Grant ZDRC2102. The As-
sociate Editor coordinating the review of this manuscript and approving it for
publication was Dr Xinxiao Wu. (Corresponding author: Cheng Deng.)
The authors are with the School of Electronic Engineering, Xidian University,
Xi’an 710071, China (e-mail: zhexu@stu.xidian.edu.cn; weikunsk@gmail.com;
xuyang.xidian@gmail.com; chdeng.xd@gmail.com).
Digital Object Identiﬁer 10.1109/TMM.2022.3205404
Fig. 1.
Illustration of different levels of supervision for VTG. (1) For full su-
pervision, the start and end time in a video corresponding to a language query are
required to be annotated. (2) For weak supervision, annotators are only asked to
identify whether the activity corresponding to a language query exists in a video
or not. (3) For point supervision, only a single temporal point corresponding to
a language query need to be annotated.
VTG [9], [12], [13]. Speciﬁcally, fully-supervised VTG requires
annotators to label the accurate start and end time in a video cor-
responding to a language query. Despite the remarkable success,
it is time-consuming and expensive to obtain such ﬁne-grained
annotations. Thus, weakly-supervised VTG is proposed to re-
duce the expensive cost of annotating temporal boundary. In
this setting, annotators are only asked to identify whether the
activity corresponding to a language query exists in a video or
not. While weakly-supervised methods can signiﬁcantly reduce
the annotation cost, their performances are apparently inferior
to the fully-supervised ones.
In order to narrow the performance gap with affordable cost,
we are motivated to introduce an intermediate form supervision,
i.e., point supervision, for VTG. Concretely, only a single tem-
poral point of a video relevant to a language query needs to be an-
notated in the point-supervised setting. Fig. 1 shows an example
of different levels of supervision for VTG. Given an untrimmed
video and a language query the person puts down the bag, full
supervision needs annotators to watch the video repeatedly to
determine the accurate start and end time corresponding to the
query, which is time-consuming and labor-intensive. For weak
supervision, annotators can only watch the video once to iden-
tify whether the activity corresponding to the query exists in the
video or not. The proposed point supervision requires negligible
extra annotating cost compared with the weakly-supervised one,
yet provides a coarse-grained location of the query.
Since the point annotation is available during training, how
to use it becomes an imperative problem for point-supervised
VTG. Intuitively, maximizing the matching score between the
annotated video point and the language query can be employed
to train the model. The extreme sparsity of label, however, is
likely to enforce the model only pay attention to the annotated
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video segments and thus leads to unsatisfying grounding per-
formance, as shown in our experiments. Recently, pseudo la-
bel generation methods [14], [15] have been employed in other
point-supervised video tasks. But these methods are limited in
video modality with pre-deﬁned categories and require anno-
tated points belonging to different classes to determine the ac-
tion boundary. In contrast, point-supervised VTG involves both
video and language modalities with complex multi-modal in-
teractions. How to locate the temporal boundary relevant to a
language query under the point supervision is a more challeng-
ing problem.
In this paper, we present a novel method, dubbed PS-VTG, for
point-supervised VTG. A language activation sequence (LAS)
is ﬁrst obtained by an attention-based grounding network. Since
there is no annotated points belonging to other classes avail-
able for reference, segment-level pseudo labels are proposed
to be generated in a “candidate + selecting” manner. Speciﬁ-
cally, a set of candidate labels are generated based on the LAS
and pre-deﬁned activity thresholds. Then the ﬁnal pseudo la-
bel is selected after ﬁltering out the irrelevant and overlapping
ones by the point supervision and Non-Maximum Suppression
(NMS). In addition, cross-modal contrast and multi-level distri-
bution calibration are introduced to further enforce the model
to precisely highlight the language-relevant segments. Our key
contributions can be summarized as follow:
1) To the best of our knowledge, we are the ﬁrst to introduce
point supervision for VTG, which aims to narrow the per-
formance gap between weakly- and fully-supervised VTG
with affordable annotation cost.
2) We present a novel method for point-supervised VTG,
where pseudo segment-level labels are generated based
on the point supervision to effectively train the model.
3) We propose cross-modal contrast and multi-level distribu-
tion calibration to precisely align the video and language,
assisting to highlight the video segments relevant to the
query.
4) Extensive experiments on three benchmark datasets
demonstrate that our method can signiﬁcantly outperform
the weakly-supervised approaches and achieve compara-
ble performance with fully-supervised ones.
II. RELATED WORK
Fully-Supervised Video Temporal Grounding. Methods of
fully-supervised VTG can be roughly divided into two cate-
gories: (1) proposal-based methods, and (2) proposal-free meth-
ods. Proposal-based methods [7], [8] follow the two-stage “pro-
posal + matching” paradigm, where a set of candidate proposals
are generated for a given video and then the most relevant time
interval is selected. CTRL [7] uses a dense sliding window to
produce activity proposals and proposes Cross-modal Temporal
Regression Localizer to generate alignment scores and location
regression results for candidate proposals. SAP [8] proposes a
framework named Semantic Activity Proposal to integrate se-
mantic information into the proposal generation process.
Since proposal-based methods need to compare all the pro-
posals with the language query, their computational costs are
extremely expensive. Therefore, proposal-free methods [11],
[16], [17], [18] are proposed to treat VTG as a regression prob-
lem and directly predict the time interval. ABLR [16] introduces
co-attention mechanism for VTG and proposes Attention Based
Location Regression to regress the temporal coordinates of lan-
guage from attention weights or attended features. LGI [11] ex-
tractes multiple semantic phrases from a language query and
presents Local Global Interactions to fuse multi-modal infor-
mation.
Despite the remarkable success, it is time-consuming and ex-
pensive to obtain the temporal boundary annotations for fully-
supervised VTG.
Weakly-Supervised Video Temporal Grounding. Weakly-
supervised VTG [9], [12], [19], [20], [21] is proposed to re-
duce the expensive cost of annotating temporal boundary for
fully-supervised VTG. TGA [9] presents Text-Guided Atten-
tion to highlight video segments relevant to a language query
and obtain a single text-dependent video feature. The network is
trained by minimizing the distance between the text-dependent
video feature and the language feature. VLANet [12] proposes
Video-LanguageAlignmentNetworktopruneoutirrelevantpro-
posals and consider various attention ﬂows to learn multi-modal
alignment. Recently, CRM [19] introduces cross-sentence tem-
poral and semantic consistency as constraints to mine the com-
plex relations in videos. Strictly speaking, however, it is not a
weakly-supervised method since temporal order is employed as
an additional supervision.
While weakly-supervised methods can signiﬁcantly reduce
the annotation cost, their performances fall largely behind the
fully-supervised ones.
Point Supervision. In order to achieve a better trade-off be-
tweentest performanceandtrainingannotationcost, point super-
vision [14], [15], [22], [23], [24], [25], [26] has been introduced
to some computer vision tasks. Bearman et al. [22] ﬁrst introduce
point supervision for semantic segmentation. Mettes et al. [23]
apply point supervision for spatio-temporal action localization
in video. Recently, SF-Net [14] proposes to use single-frame
supervision for video temporal action localization. Li et al. [15]
present a temporal action segmentation model using only times-
tamps annotations.
In this paper, we introduce the point supervision for VTG.
Different previous tasks which are limited in uni-modality and
pre-deﬁned categories, VTG involves complex video and lan-
guage interactions, determining PS-VTG to be more suitable
for real-world situations.
III. PROPOSED METHOD
Given an untrimmed video V , a language query Q, and a point
annotation tp, point-supervised VTG aims to learn a model f
that can predict the time interval I = (ts, te) in the video cor-
responding to the query: f : (V, Q, tp) →I. ts and te represent
the start and end time, respectively. Notably, tp is a timestamp
within I, i.e., ts ≤tp ≤te, and the only supervision available
during training.
Fig. 2 shows the framework of our proposed method. Video
segment features and word-level language features are ﬁrst
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XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING
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Fig. 2.
The framework of our proposed point-supervised VTG method. Given video segment features and word-level query features extracted from pre-trained
models, a ﬁne-grained interaction framework is ﬁrst employed to fuse the multi-modal information. Then we obtain the language activation sequence (LAS) based
on video segment features and pooled query feature. Pseudo label is generated by the point supervision and predicted LAS. In addition, cross-modal contrast and
multi-level distribution calibration are proposed to precisely align the video and language.
Fig. 3.
Illustration of the proposed pseudo label generator.
extracted from pre-trained video and language models, respec-
tively. Then we fuse the video and language features with a
ﬁne-grained interaction network to obtain the language acti-
vation sequence (LAS). The elaborated label generator is em-
ployed to generate segment-level pseudo label based on the
point supervision and LAS. In addition, cross-modal contrast
and multi-level distribution calibration are performed to better
align the video with the language query.
In the following, we elaborate the main components of the
proposed method, i.e., (1) grounding network, (2) pseudo label
generation, (3) cross-modal contrast, (4) multi-level distribution
calibration, and (5) training and inference.
A. Grounding Network
Suppose
Xi ∈RdV ×LV
and
Qi ∈RdQ×LQ
(i =
1, 2, . . . , N) as the video segment features and query word
features extracted from pre-trained video and language models,
respectively. dV is the dimension of video segment features, dQ
is the dimension of query word features, LV is the number of
segments per video, LQ is the number of words per sentence,
and N is the batch-size. We ﬁrst embed Xi and Qi into a com-
mon space with dimension d. Then a ﬁne-grained interaction
network are employed to obtain semantic-aware video segment
features V i ∈Rd×LV and global query feature qi ∈Rd.
Speciﬁcally, segment-wise query representation Si ∈Rd×LV
is ﬁrst learned by attending word-level features based on each
video segment feature:
Si
t =
LQ

l=1
λtlQi
l,
(1)
where λtl is the normalized attention weight computed by
the t-th segment feature, i.e, λtl =
exp(rtl)
LQ
k=1 exp(rtk)
and rtl =
wT
r tanh(WQQi
l + WXXi
t). WQ, WX ∈Rd×d and wT
r are
the learnable matrices.
Then we fuse Si and Xi to obtain semantic-aware video
segment features V i:
V i
t = WV((Xi
t ⊙Si
t)||Si
t)),
(2)
where WV ∈Rd×2 d is a learnable embedding matrix. ⊙and ||
represent the element-wise multiplication and the concatenation
of two features, respectively.
In addition, the global query feature qi is obtained by
weighted pooling:
qi = QiSoftmax(WGQi)⊤,
(3)
where WG ∈R1×d is a learnable matrix.
LAS ai is obtained based on V i and qi:
ai
t = Sigmoid(Wa(V i
t||qi)),
(4)
where V i
t (t = 1, 2, . . . , LV ) is the t-th segment feature of V i
and ai
t is the predicted activation value of V i
t. || represents the
concatenation of two features and Wa ∈R1×2 d is a learnable
embedding matrix.
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B. Pseudo Label Generation
Since point-annotation is available in the proposed point-
supervised setting, how to use it to train the grounding model be-
comes a imperative problem. In this paper, we present a pseudo
label generator, as shown in Fig. 3, to generate segment-level
pseudo label based on the point supervision and LAS.
Given the predicted LAS ai, we ﬁrst generate a set of candi-
dates according to pre-deﬁned activity thresholds. Speciﬁcally,
a video clip is selected to be a candidate if the activation values
within it consecutively greater than the threshold. In addition, a
matching score is computed for each candidate. Taking a can-
didate (ts, te) as an example, the matching score m is deﬁned
as:
m = 1
l
te

t=ts
ai
t −
1
LV −l
ts−1

t=1
ai
t +
LV

t=te+1
ai
t

,
(5)
where ts, te, and l = te −ts + 1 are the start index, end index,
and length of the candidate, respectively. The ﬁrst and second
terms represent the mean activation of the candidate and its tem-
poral neighbors, respectively.
Then the candidates that do not include the point annotation
will be ﬁltered out. Non-Maximum Suppression (NMS) is per-
formed to remove overlapping candidates and pseudo label p is
obtained from the left candidates with greatest matching score:
pi
t =

1, ˆ
ts ≤t ≤ˆ
te
0, otherwise
(t = 1, 2, . . . , LV ) ,
(6)
where ˆ
ts and ˆ
te are the start and end index of the selected can-
didate, respectively.
The binary cross entropy loss is employed to enforce the pre-
dicted ai to be close to the generated pseudo label pi:
LBCE = 1
N
N

i=1

pi log ai +

1 −pi
log

1 −ai
.
(7)
Since the generated pseudo label is binary and includes the
point supervision, the model trained with LBCE will gradually
highlight the video segments semantically relevant to the lan-
guage query.
C. Cross-Modal Contrast
In order to precisely align the video and language, we present
cross-modal contrast based on the point supervision. Speciﬁ-
cally, given a language query qi as an anchor, we select video
segment vi including the point annotation as a positive sam-
ple. Negative samples come from both the intra- and inter
videos, i.e., unmatched segments determined by the pseudo label
and segments corresponding to other queries in the mini-batch.
Video2query contrastive loss LCON_V 2Q is employed to ob-
tain discriminative representation by pulling the anchor and the
positive sample together while pushing away the anchor from
Fig. 4.
Illustration of the optimal distribution of LAS and two local optimums.
negative samples, which can be deﬁned as:
LCON_V 2Q = −1
N
N

i=1
× log
es(qi,vi)/τ
es(qi,vi)/τ +N
k̸=i es(qi,vk)/τ +M
j=1 es(qi,vj)/τ ,
(8)
where s(x, y) = x⊤y/||x||||y|| denotes cosine similarity and τ
is the temperature parameter. N and M are the batch-size and
the number of negative intra-video samples, respectively.
Analogously, we deﬁne the query2video contrastive loss
where video segment is treated as an anchor and queries as con-
trastive samples:
LCON_Q2V = −1
N
N

i=1
log
es(vi,qi)/τ
es(vi,qi)/τ + N
k̸=i es(vi,qk)/τ.
(9)
The total cross-modal contrastive loss is the sum of
video2query and query2video contrastive loss:
LCON = LCON_V 2Q + LCON_Q2V .
(10)
D. Multi-Level Distribution Calibration
Ideally, the distribution of predicted ai along time is plateau-
like, i.e., the activation values of query-relevant segments in
the middle are close to 1 while close to 0 otherwise. To this
end, we propose multi-level distribution calibration to restrict
the distribution of predicted language activation sequence and
enforce the model to precisely focus on the relevant segments.
For intra-video distribution calibration, the mean activation
within the time interval of pseudo label is encouraged to be
higher than that outside the interval. The intra-video distribution
calibration loss is thus deﬁned as:
LMDC_R =
1
N
N

i=1
max
⎛
⎝0,
ˆ
ts−1
t=1 ai
t + LV
t= ˆ
te+1 ai
t
LV −ˆ
l
−
ˆ
te
t= ˆ
ts ai
t
ˆ
l
+ δ
⎞
⎠,
(11)
where ˆ
ts, ˆ
te, and ˆ
l = ˆ
te −ˆ
ts + 1 represent the start index, end
index and length of the pseudo label, respectively. LV is the
number of segments per video and δ is the margin (set to 0.5).
Despite the ability of intra-video distribution calibration to
make the activation within and outside the interval distinguish-
able, the predicted ai is still likely to be local optimum. Con-
cretely, the value of ai may close to 1 only at the annotated point
or at every points, i.e., LV
t=1 ai
t = 1 or LV
t=1 ai
t = LV . Fig. 4
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XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING
6125
shows the distribution of the optimal LAS (4a) and two local
optimums (4b and 4c).
To avoid the local optimums, we propose inter-video distribu-
tion calibration. The mean activation of videos in a mini-batch
is forced to subject a Gaussian distribution with middle value
mean. The inter-video distribution calibration loss can be de-
ﬁned as:
LMDC_E =

1
N
N

i=1
LV
t=1 ai
t
LV
−μ

1
N
N

i=1
LV
t=1

ai
t −
LV
t=1 ai
t
LV
2
LV
−σ

,
(12)
where the ﬁrst and the second terms of the loss function are
employed to restrain the mean and variance of the predicted
activations, respectively. μ and σ are the mean and variance
of pre-deﬁned Gaussian distribution, respectively. As veriﬁed
later in the experiments, a middle value mean will encourage
the model to precisely focus on the segments relevant to the
language query.
The multi-level distribution calibration loss LMDC is the sum
of intra- and inter-video distribution calibration loss:
LMDC = LMDC_R + LMDC_E.
(13)
E. Training and Inference
In summary, three loss functions are employed for training,
namely: binary cross entropy loss LBCE, cross-modal con-
trastive loss LCON, and multi-level distribution calibration loss
LMDC. The overall objective function can be formulated as:
L = LBCE + αLCON + βLMDC,
(14)
where α and β are the weighting coefﬁcients for the cross-modal
contrastive loss and the multi-level distribution calibration loss,
respectively.
During inference, given an untrimmed video and a language
query, we ﬁrst obtain the LAS by the grounding network. Then
the ﬁnal grounding result is determined by a similar process
to pseudo label generation without ﬁltering out candidates ir-
relevant to point supervision, i.e., generating candidates then
selecting top1 by NMS.
IV. EXPERIMENTS
In this section, the datasets and evaluation metrics we used are
ﬁrst introduced in detail. Then, we present the implementation
details and the simulation of point supervision of our method.
Finally, the analysis, comparison with state-of-the-art methods,
and qualitative results further prove the effectiveness of our pro-
posed approach.
A. Datasets
We evaluate the proposed method on three public datasets:
ActivityNet Captions [27], Charades-STA [7], and TACoS [28].
ActivityNet Captions. The ActivityNet Captions dataset
contains 10,024, 4,926 and 5,044 videos for training, validation,
and testing, respectively. The average length of language query
and average number of activities per video are 13.48 words and
3.65, respectively. We use the validation set for evaluation since
the testing set is not publicly available.
Charades-STA. The Charades-STA dataset is built on Cha-
rades dataset for video temporal grounding. It contains 12,408
and 3,720 clip-language pairs for training and testing, respec-
tively. The average length of language query, average duration of
videos and average number of activities per video are 8.6 words,
29.8 seconds, and 2.3 respectively.
TACoS. The TACoS dataset is built on MPII-Compositive
dataset and consists of 10,146, 4,589, and 4,083 instances for
training, validation, and testing, respectively. The average dura-
tion of videos is around 300 s.
B. Evaluation Metrics
Following previous works, we report two metrics to measure
the performance of video temporal grounding: IoU = n and
mIoU.
Rank@1 IoU = n. Rank@1 IoU = n is referred to as the
percentage of test samples whose Intersection over Union (IoU)
with ground-truth (GT) is higher than n. n = {0.3, 0.5, 0.7} are
reported in our experiments.
mIoU. mIoU is the mean IoU over all test samples.
C. Implementation Details
We use pre-trained C3D [29], I3D [30], and C3D models to
extract video features for the ActivityNet Captions, Charades-
STA, and TACoS datasets, respectively. Each video is uniformly
sampled into LV = 128 segments and embedded into a dV ×
LV dimensional representation space by an embedding matrix.
Glove [31] is employed to extract word features of the language
query. The dimensions of the video segment dV , the language
query dQ, and the common space d are set to 1024, 300, and
128, respectively.
Our approach is implemented with PyTorch [32] and opti-
mized by ADAM [33] optimizer with a learning rate of 0.0001.
We set the batch size N to 64 and training epoch to 200. The
loss weights α and β are determined by the grid search and set
to 0.1 and 0.1 respectively. In addition, the activity thresholds
for generating candidates and the threshold in NMS for ﬁltering
out overlapping are set to (0.4, 0.6, 0.1) and 0.4, respectively.
The temperature parameter τ in cross-modal contrast is set to
1. μ and σ for inter-video distribution calibration are set to (0.3,
0.1), (0.3, 0.01), and (0.1, 0.01) on the ActivityNet Captions,
Charades-STA, and TACoS datasets, respectively.
D. Simulation of Point Supervision
Since the datasets used for evaluation do not have point an-
notations themselves, we simulate the point supervision based
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TABLE I
PERFORMANCE COMPARISON WITH WEAKLY- AND FULLY-SUPERVISED
COUNTERPARTS ON THE ACTIVITYNET CAPTIONS, CHARADES-STA, AND
TACOS DATASETS
on the existing boundary annotations. Following the strategy
in [34], we generate two sets of point annotations, i.e., PS-U and
PS-G, from different distributions. Speciﬁcally, given s and e as
the start and end time corresponding to a language query, the
point annotation belonging to PS-U is randomly sampled from
the uniform distribution [s, e]. In addition, we randomly sample
points from a Gaussian distribution with mean (s + e)/2 and
standard deviation 1 s to form the set PS-G.
E. Analysis
Gains from the point supervision. To investigate the gains
of point supervision, we compare our point-supervised method
with its weakly- and fully-supervised counterparts. Speciﬁcally,
the weakly-supervised model is trained only by cross-modal
contrastive loss, where the video segments are pooled by the
predicted language activation sequence to obtain a video fea-
ture. For fully-supervised counterpart, we replace the gener-
ated pseudo label with ground-truth one to train the model. Our
point-supervised model is trained with the binary cross entropy
loss LBCE, cross-modal contrastive loss LCON, and multi-level
distribution calibration loss LMDC.
Table I shows the results under different levels of supervi-
sion on the ActivityNet Captions, Charades-STA, and TACoS
datasets. Our model trained with point supervision outperform
the weakly-supervised counterpart with a large margin, e.g.,
32.17%, 27.20%, 14.47% and 21.26% in terms of IoU = 0.3,
IoU = 0.5, IoU = 0.7, and mIoU on the ActivityNet Captions
dataset.Inaddition,comparableresultswiththefully-supervised
counterpart are achieved by the point-supervised model. Consid-
ering that point supervision requires negligible extra annotation
cost comparing with weak supervision, the effectiveness of point
supervision is clearly veriﬁed in this experiment.
The annotation cost of point supervision. Given an
untrimmed video and the language query, full supervision needs
annotators to watch the video repeatedly to determine the ac-
curate start and end time corresponding to the query, which
is time-consuming and labor-intensive. For weak supervision,
annotators can only watch the video once to identify whether
TABLE II
THE ANNOTATION COST OF DIFFERENT SUPERVISIONS ON THE ACTIVITYNET
CAPTIONS DATASET
TABLE III
EFFECTIVENESS OF PSEUDO LABEL GENERATION ON THE ACTIVITYNET
CAPTIONS, CHARADES-STA, AND TACOS DATASETS. LCON,
LCON + LBCE−P S, AND LCON + LBCE DIFFER IN THE USE OF POINT
SUPERVISION
the activity corresponding to the query exists in the video or not.
The proposed point supervision requires only a few extra pauses
to annotate a temporal point, which is negligible extra annotating
cost compared with the weakly-supervised one. In order to illus-
trate the differences among supervisions, we randomly annotate
100 instances of the ActivityNet Captions dataset.
Table II shows the average annotating time of three supervi-
sions. The average annotating time at full, point, and weak levels
are 504 s, 185 s, and 180 s, while the mIoU are 43.73%, 41.49%,
and 20.23%, respectively. The results proves our method obtains
impressive improvement with little increase of annotation cost
compared with weakly-supervised methods.
Effectivenessofpseudolabelgeneration.Givenpointsuper-
visionavailableduringtraining,howtouseitbecomestheimper-
ative problem for point-supervised VTG. In this paper, we pro-
vide a novel solution. i.e., generating segment-level pseudo label
based on the point supervision and predicted LAS. To investi-
gate the effectiveness of pseudo label generation, we compare
our method with two baseline approaches in this experiment:
(1) LCON and (2) LCON + LBCE−P S. Speciﬁcally, LCON is
the baseline approach trained only with cross-modal contrastive
loss. In this case, point supervision is used to select segment
as contrastive samples from a video. The baseline approach
LCON + LBCE−P S is trained with cross-modal contrastive loss
and binary cross entropy loss only at the annotated point. In con-
trast, our method is trained with cross-modal contrastive loss and
binary cross entropy loss at all points, i.e., LCON and LBCE.
As shown in Table III, our method trained with LCON and
LBCE signiﬁcantly outperform the two baseline approaches on
the ActivityNet Captions, Charades-STA, and TACoS datasets,
which indicates the effectiveness of the proposed pseudo label
generation.
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XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING
6127
Fig. 5.
Loss and mIoU vs. iteration using the binary cross entropy LBCE on
the Charades-STA dataset.
TABLE IV
INFLUENCE OF POINT SUPERVISION SIMULATION METHODS ON THE
ACTIVITYNET CAPTIONS, CHARADES-STA, AND TACOS DATASETS. PS-U AND
PS-G REPRESENT POINT ANNOTATIONS SIMULATED FROM DIFFERENT
DISTRIBUTIONS
In order to investigate the convergence of training LAS using
the generated pseudo label. We train the model with only the bi-
nary cross entropy LBCE and present the training loss as well as
mIoU during iteration on the Charades-STA dataset. As shown
in Fig. 5, the loss decreases and mIoU increases during the train-
ing process, which veriﬁes the convergence of training LAS us-
ing the generated pseudo label. We attribute the convergence to
elaborated pseudo label generator: (1) The candidates are
generated according to pre-deﬁned activity thresholds (set to
(0.4:0.6:0.1) in the experiments), which guarantees the consec-
utiveness of the activation values in a candidate. (2) The candi-
dates that do not include the point supervision are ﬁltered out,
which makes full use of the point information. (3) The pseudo
label is set to be binary while the LAS is continuous. Minimizing
LBCE will enforce the predicted LAS to be close to the gener-
ated pseudo label and make the model gradually highlight the
video segments semantically relevant to the language query.
Inﬂuence of point supervision simulation methods. As dis-
cussed in the last subsection, we simulate two sets of point-
annotations, i.e., PS-U and PS-G, from different distributions. In
this experiment, we aim to investigate the inﬂuence of the point
supervision simulation methods. The models are trained with the
binary cross entropy loss LBCE, cross-modal contrastive loss
LCON, and multi-level distribution calibration loss LMDC on
the ActivityNet Captions, Charades-STA, and TACoS datasets.
Table IV presents the experimental results. Similar results are
obtainedbasedonthetwosimulationmethods.Pointannotations
from PS-U are empolyed in rest experiments.
Contribution of multi-level distribution calibration. In or-
der to verify the effectiveness of multi-level distribution calibra-
tion, we present an ablation study on the ActivityNet Captions,
TABLE V
EFFECTIVENESS OF MULTI-LEVEL DISTRIBUTION CALIBRATION ON THE
ACTIVITYNET CAPTIONS, CHARADES-STA, AND TACOS DATASETS. MDC_R,
MDC_E, AND MDC REPRESENT THE INTRA-VIDEO DISTRIBUTION
CALIBRATION, INTER-VIDEO DISTRIBUTION CALIBRATION, AND OVERALL
MULTI-LEVEL DISTRIBUTION CALIBRATION, RESPECTIVELY
TABLE VI
CHOICE OF μ AND σ ON THE ACTIVITYNET CAPTIONS DATASET
Charades-STA,andTACoSdatasets.Fourvariantsofourmethod
are compared in this experiment: (1) PS-VTG, (2) w/o MDC_R,
(3) w/o MDC_E, and (4) w/o MDC. Speciﬁcally, PS-VTG is
our full model trained with the binary cross entropy loss LBCE,
cross-modal contrastive loss LCON, and multi-level distribu-
tion calibration loss LMDC. w/o MDC_R, w/o MDC_E, and w/o
MDC are the full model without intra-video distribution calibra-
tion, inter-video distribution calibration, and overall multi-level
distribution calibration, respectively.
We show the experimental results in Table V. The perfor-
mance largely drops when the intra-video or inter-video distri-
bution calibration is removed, which indicates the effectiveness
of multi-level distribution calibration.
Choice of μ and σ. Inter-video distribution calibration is pro-
posed to force the mean activation of videos in a mini-batch sub-
ject a Gaussian distribution with middle value mean. We perform
experiments with different Gaussian distribution to determine
the optimal mean μ and variance σ of the Gaussian distribution.
Table VI shows the results on the ActivityNet Captions
dataset. A middle mean can bring about different improvements
andtheoptimalperformanceisobtainedwhenμ = 0.3, σ = 0.1.
F. Comparison With State-of-The-Art Methods
We compare the proposed method with several state-of-the-
art methods at different levels of supervison. Among these
methods, TGA [9], WSLLN [35], VLANet [12], SCN [20],
RTBPN
[13], BAR [21], LCN [36], and LoGAN [45] are
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IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 25, 2023
weakly-supervised methods. CRM [19], as discussed in re-
lated works, requires additional temporal order supervision and
thus is a special weakly-supervised approach. While CTRL [7],
SAP[8],MLVI[37],TripNet[38],RWM[39],DEBUG[40],2D-
TAN [10], ABLR [16], GDP [17], VSLNet [41], LGI [11],
DRN [42], PMI-LOC [18], IVG-DCL [43] and MMN [44]
are fully-supervised ones. To make the presented paper self-
contained,wegiveabriefintroductionofthemethodsmentioned
above.
r TGA presents Text-Guided Attention to highlight video
segments relevant to a language query and obtain a single
text-dependent video feature. The network is trained by
minimizing the distance between the text-dependent video
feature and the language feature.
r WSLLN proposes a two-branch network to measure
segment-text consistency and conduct segment selection
(conditioned on the text) simultaneously.
r VLANet proposes Video-Language Alignment Network to
prune out irrelevant proposals and consider various atten-
tion ﬂows to learn multimodal alignment.
r SCN proposes a Semantic Completion Network to measure
the semantic similarity between proposals and query.
r RTBPN presents a Regularized Two-Branch Proposal Net-
work for weakly-supervised VTG. A language-aware ﬁlter
is employed to generate the enhanced and suppressed video
streams to generate positive and negative proposals.
r BAR proposes Boundary Adaptive Reﬁnement framework
that resorts to reinforcement learning to guide the process
of progressively reﬁning the temporal boundary.
r LCN proposes a Local Correspondence Network which
consists of hierarchical feature representation, cycle-
consistent local correspondence, and relation modeling
among candidate moments to explore the ﬁne-grained cor-
respondences between video and text.
r CRM introduces cross-sentence temporal and semantic
consistency as constraints to mine the complex relations
in videos, where temporal order is employed as an addi-
tional supervision.
r CTRL is a pioneering VTG work. It leverages sliding win-
dowtoobtaincandidateclipsofvariouslengthsandfusethe
candidate representations with the sentence representation
by three operators (i.e.,add, multiply, and fully-connected
layer) to predict the alignment score.
r SAP proposes a Semantic Activity Proposal framework
that integrates the semantic information of sentence queries
into the proposal generation process to get discriminative
activity proposals. Visual and semantic information are
jointly utilized for proposal ranking and reﬁnement.
r MLVI introduces a multilevel feature integration model to
fuse language and vision earlier and more tightly.
r MAN presents a Moment Alignment Network where lan-
guage query is integrated as dynamic ﬁlters. In addition,
an iterative graph adjustment network is devised to model
moment-wise temporal relations.
r TripNet introduces an end-to-end reinforcement learning
framework that uses a gated-attention mechanism over
cross-modal features.
TABLE VII
PERFORMANCE COMPARISON WITH STATE-OF-THE-ART METHODS AT
DIFFERENT LEVELS OF SUPERVISION ON THE ACTIVITYNET CAPTIONS
DATASET. * INDICATES ADDITIONAL SUPERVISION IS EMPLOYED
r RWM views VTG as controlling an agent to read the de-
scription, to watch the video as well as the current localiza-
tion, and then to move the temporal grounding boundaries
iteratively to ﬁnd the best matching clip.
r 2D-TAN proposes to model the temporal relations between
video candidates by a two-dimensional map, where one
dimension indicates the starting time of a moment and the
other indicates the end time. The 2D temporal map can
cover diverse video moments with different lengths, while
representing their adjacent relations.
r ABLR proposes a Attention Based Location Regression
model to directly regress the temporal coordinates from the
global attention outputs with a multi-modal co-attention
mechanism.
r GDP designs a novel bottom-up model: Graph-FPN with
Dense Predictions. It ﬁrst generates a frame feature pyra-
mid to capture multi-level semantics, then utilizes graph
convolution to encode the plentiful scene relationships.
r VSLNet proposes a video span localizing network on top of
the standard span-based QA framework with query-guided
highlighting strategy.
r LGI introduces a sequential query attention module to
extract representations of multiple and distinct semantic
phrases from a text query. Then Local-Global video-text
Interaction algorithm is employed to model the relationship
between video segments and semantic phrases in multiple
levels.
r DRN proposes a Dense Regression Network for VTG,
which provides a new perspective to leverage dense su-
pervision from the sparse annotations.
r PMI-LOC proposes pairwise modality interaction in both
the sequence and channel levels to better understand video
contents.
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XU et al.: POINT-SUPERVISED VIDEO TEMPORAL GROUNDING
6129
Fig. 6.
The qualitative results of different models on the Charades-STA and ActivityNet Captions datasets. GT is the ground-truth time interval. Ours, w/o
MDC, and Base Model represent the proposed PS-VTG model, PS-VTG without multi-level distribution calibration, and model without pseudo label generation,
respectively.
TABLE VIII
PERFORMANCE COMPARISON WITH STATE-OF-THE-ART METHODS AT
DIFFERENT LEVELS OF SUPERVISION ON THE CHARADES-STA DATASET. *
INDICATES ADDITIONAL SUPERVISION IS EMPLOYED
r IVG-DCL proposes interventional video grounding to
eliminate the spurious correlations between query and
video features based on causal inference. In addition, a
dual contrastive learning approach is employed to better
align the text and video.
TABLE IX
PERFORMANCE COMPARISON WITH STATE-OF-THE-ART METHODS AT
DIFFERENT LEVELS OF SUPERVISION ON THE TACOS DATASET
r MMN presents a Mutual Matching Network on a perspec-
tive of metric-learning to directly model the similarity
between language queries and video moments in a joint
embedding space.
Tables VII, VIII, and IX show the results on the ActivityNet
Captions, Charades-STA, and TACoS datasets, respectively. Our
approach outperforms all the weakly-supervised methods and
achieve comparable results with fully-supervised ones. For ex-
ample, BAR is the best weakly-supervised VTG method on the
ActivityNet Captions dataset and our approach outperforms it
with a large margin of 10.68% and 8.86% in terms of IoU = 0.3
and IoU = 0.5, respectively. In addition, our approach trained
with point-supervision can even outperform some recent fully-
supervised methods, e.g., CTRL, MLVI, TripNet, RWM, ABLR,
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DEBUG, GDP, LGI, and PMI-LOC on the ActivityNet Captions
dataset.
G. Qualitative Results
In Fig. 6, we show the qualitative results of different mod-
els. Our proposed model PS-VTG achieves more accurate video
temporal grounding than w/o MDC without multi-level distribu-
tion calibration and Base Model without pseudo label generation
(i.e., trained with cross-model contrastive loss and binary cross
entropy loss only at annotated point), demonstrating the effec-
tiveness of our method.
V. CONCLUSION AND FUTURE WORK
In this paper, we introduce point supervision for VTG, which
can effectively narrow the performance gap between weakly-
and fully-supervised VTG with affordable annotation cost. To
make full use of the point supervision, we generate pseudo
segment-level labels based on the point supervision and the pre-
dicted language activation sequence. In addition, cross-modal
contrast and multi-level distribution calibration are proposed to
precisely align the video and language. Extensive experiments
on three benchmark datasets demonstrate that our method can
signiﬁcantly outperform the weakly-supervised approaches and
achieve comparable performance with fully-supervised ones.
Existing VTG methods rely on large-scale labeled data to train
a model, which is time-consuming and expensive to obtain. In
the future, we will explore to solve the VTG problem under the
limited data [46], [47] and the limited supervision settings [48],
[49], e.g., Semi-Supervised VTG, and Unsupervised VTG.
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Zhe Xu received the B.E. degree in 2019 from Xi-
dian University, Xi’an, China, where he is currently
working toward the Ph.D. degree with the School of
Electronic Engineering. His research interests include
computer vision and machine learning.
Kun Wei received the B.E. and Ph.D. degrees in
electronic and information engineering from Xidian
University, Xi’an, China, in 2017 and 2022, respec-
tively. He is currently a Lecturer with the School of
Electronic Engineering, Xidian University. His re-
search interests include computer vision and machine
learning.
Xu Yang (Member, IEEE) received the B.E. and
Ph.D. degrees in electronic and information engineer-
ing from Xidian University, Xi’an, China, in 2021. He
is currently a Lecturer with the School of Electronic
Engineering, Xidian University. His research inter-
ests include computer vision and machine learning.
Cheng Deng (Senior Member, IEEE) received the
B.E., M.S., and Ph.D. degrees in signal and informa-
tion processing from Xidian University, Xi’an, China.
He is currently a Full Professor with the School of
Electronic Engineering, Xidian University. Dr. Deng
is author or coauthor of more than 100 scientiﬁc arti-
cles at top venues, including IEEE TRANSACTIONS ON
PATTERN ANALYSIS AND MACHINE INTELLIGENCE,
IEEE TRANSACTIONS ON NEURAL NETWORKS AND
LEARNING SYSTEMS, IEEE TRANSACTIONS ON IM-
AGE PROCESSING, IEEE TRANSACTIONS ON CYBER-
NETICS, NeurIPS, ICML, CVPR, ICCV, AAAI, IJCAI, and KDD. His research
interests include computer vision, pattern recognition, and information hiding.
Authorized licensed use limited to: XIDIAN UNIVERSITY. Downloaded on November 11,2023 at 03:52:33 UTC from IEEE Xplore. Restrictions apply.

Original LaTeX notation

exp(𝑆(𝑝𝑏,𝑖, 𝑭𝑠
𝑗)/𝜏) + exp(𝑆(𝑝𝑗, 𝑭𝑠
𝑏)/𝜏)

,
𝛽= (1 −¯
𝜇)
¯
𝜇(1 −¯
𝜇)
¯
𝜎2
−1

«
−

t=ts
ai
t −
1
LV −l
ts−1

t=1
ai
t +
LV

t=te+1
ai
t

i=1

pi log ai +

1 −pi
log

1
N
N

i=1
LV
t=1 ai
t
LV
−μ

1
N
N

i=1
LV
t=1

ai
t −
LV
t=1 ai
t
LV
2
LV
−σ

choice A

Point-Supervised Video Temporal Grounding

choice B

Probability Distribution Based Frame-supervised Language-driven Action Localization

choice C

Mm - 2024 - Explicit Granularity and Implicit Scale Correspond.pdf

choice D

D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation

difficulty

easy

domain

Multi-Document QA

length

medium

sub domain

Academic

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Source and history

upstream_id / Original problem ID: 66ed364d821e116aacb1f47e
task_id: b371f052-dc42-58df-a732-569f0d3396d9
task_key: train--66ed364d821e116aacb1f47e
task_revision_id: 3
config / split: / train

Official source

initial import