Towards Subject-Oriented Video Captioning via User-Specified Targets

arXiv:2312.13330 · cs.CV · Submitted 2023-12-20 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Towards Subject-Oriented Video Captioning via User-Specified Targets".

Jane: Describing video content according to specific user interests remains a significant challenge in video captioning because existing methods often generate captions that focus on general entities rather than a user-specified…

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So we're talking about this paper called "Towards Subject-Oriented Video Captioning via User-Specified Targets." Basically, they're tackling the problem that current video captioning models often describe the whole video instead of what a user actually wants them to focus on.

Jane: That’s right, and they propose a new task called Subject-Oriented Video Captioning or SOVC, which lets users pick a target using a bounding box to specify what should be described in the caption.

Lu: The core idea is that instead of just generating general descriptions, the model needs to focus its attention on the specific activities happening around that user-defined subject.

Meng: So they're saying that existing methods, which often sample fixed frames uniformly, aren't good enough because they miss the subject’s actual actions.

Lalam: Exactly, and to fix that, they introduce SOVCNet with two main parts: a sampling module and an encoding module designed specifically for this subject orientation.

Tom: That makes sense. So how does this new method actually work to keep the focus on that specific subject?

Jane: Well, the paper explains that the goal is to move past uniformly sampling fixed frames and instead develop methods that describe what the subject is doing.

Lu: The subject-oriented sampling module handles finding relevant information by using a two-step strategy of clustering and then sampling.

Meng: So they first extract features for the subject frame and all other frames using something like Resnet50, then they use K-means to group those frames into clusters.

Lalam: Right, and the key part is that in each cluster, they pick subject-related frames based on how similar their features are to the subject frame's features using cosine similarity.

Tom: That sounds like a way to filter out all that irrelevant background noise and just grab the important moments related to the person or object of interest.

Jane: And then they feed those selected frames into the encoding module, which is where they direct the video encoder’s attention toward those subject activities.

Lu: The encoding module uses features from subject areas as hard prompts, along with patch embedding on the video frames and learnable soft prompts to help adapt to the final generation task.

Meng: So it's using both explicit subject information and these trainable soft parameters to really tune the model’s focus onto what matters.

Paper summary: Lalam: Yeah, that joint representation of all those tokens is then fed into the video encoder to create a much better feature representation for generating captions centered on the subject.

Tom: That sounds like they're building a very targeted input stream for the language part of things. What about how they got the data to train this?

Jane: They built two new datasets, SO-MSVD and SO-MSRVTT, by re-annotating existing general captioning sets called MSVD and MSRVTT.

Lu: The annotation process was pretty detailed; it involved using spaCy to pull entities, which they call subjects, while discarding captions with abstract subjects like "a video".

Meng: Then they used object detection with Faster-RCNN to find the bounding boxes for each subject in the videos and ranked entity categories against those subject words.

Lalam: After that, there was a manual check step to correct any missing or incorrect bounding boxes before organizing everything into a set called V, S, C where S are the annotated subject regions.

Tom: So they took existing data and added this extra layer of subject-specific labeling to make the training task more focused.

Jane: Exactly, and that allowed them to test how SOVCNet performs on describing specific entities compared to just general video captioning.

Lu: In terms of results, they found that the four extended baseline models they tested struggled with their new task, showing a "large performance drop on our new task compared to the performance on the original video captioning task."

Meng: But SOVCNet did things differently; it actually achieved the best performance across all metrics when compared to those other extended methods.

Lalam: They specifically mentioned that SOVCNet gained four point zero and one point one absolute improvements in terms of the main metric CIDEr on SO-MSRVTT and SO-MSVD, respectively, compared to the second-best SwinBERT extension they tested <ref:2312.13330#pg2>.

Tom: And they also highlighted a specific measure of success for this method where the accuracy in predicting the subject specified by the user was highest, hitting sixty-five point seven percent on SO-MSVD and sixty-three point seven percent on SO-MSRVTT.

Jane: That's a really concrete number showing that it’s not just a slight improvement, but a significant jump in accuracy for identifying what the user actually asked for.

Lu: The ablation study showed that the subject-oriented sampling module made the "greatest contribution," increasing the CIDEr score to fifty-nine point two from fifty-seven point one on SO-MSRVTT.

Paper summary: Meng: And it seems that using a cluster-based sampling method got them to a high CIDEr score of sixty-one point one, which was about two points better than the others they tested by nearly ten percent.

Lalam: They also explored different numbers for the number of soft tokens and found that five is the better choice for getting a higher CIDEr score on SO-MSRVTT.

Tom: It sounds like they’ve done a lot of fine-tuning on these internal modules to see what works best for focusing the model. What's the big picture implication here?

Jane: The qualitative results showed that SOVCNet really focuses better and describes entities of interest more accurately than those baseline models, even in specific examples.

Lu: For instance, when comparing it to the extended SwinBERT, SOVCNet accurately described "two men are talking" when the other model got it wrong by describing it as "a man is talking about a wrestling match."

Meng: It also captured more detail effectively, like describing the "playing guitar" action in one example that the other models missed.

Lalam: This means for applications, like creating automated video descriptions for specific scenes, this method is much better at getting those details right.

Tom: So to wrap up this paper on "Towards Subject-Oriented Video Captioning via User-Specified Targets," it’s about introducing a new way to train models specifically to answer user questions about what's happening in a video.

Jane: The authors, Chang Teng, Yunchuan Ma, Guorong Li, and others, show that by creating this task and the SOVCNet architecture with its sampling and encoding modules, you can get captions that are much more focused on the entity the user cares about.

Lu: It paves the way for making video understanding models useful in real-world scenarios where you don't just want a general summary, but a precise description of a specific action or object.

Meng: From an engineering standpoint, it means we can design systems that don't waste processing power on irrelevant parts of the video when they know exactly what subject to look for.

Lalam: And in terms of how this advances AI culture, it suggests that future video generation and understanding tools will be much more responsive to specific user needs rather than just producing generic descriptions.

Tom: That’s the gist of it, moving from general video description to highly targeted subject description using SOVCNet. We'll be talking more about the practical implications of this next time.

Conclusion: Tom: So we've seen how this Subject-Oriented Video Captioning paper works—it’s all about letting users pick exactly what they want described in a video caption, and the authors call their new method SOVCNet.

Jane: They did this by building two main parts, a sampling module to find the right frames and an encoding module that uses subject features as hard prompts. It sounds like they really focused on making sure the model pays attention only to what’s important for that specific person or object in the video.

Lu: I think what's interesting is how they used those subject features as both direct instructions and soft prompts, which lets the AI adapt better to generating that targeted description. It’s like giving it a very clear blueprint before it starts writing the script.

Meng: From an engineering view, that focus helps us stop wasting computing power processing all the irrelevant background noise in a video when we know exactly what we need to capture. That’s practical for real-time systems, I think.

Lalam: For me, this means future AI tools won't just give you a general summary; they could give you a precise description of exactly what’s happening around that one specific person or item. It shifts the focus from broad understanding to deep, targeted detail.

Tom: So for the listener who’s just tuning in, what does this actually mean? Basically, it means video AI gets much better at answering specific questions about a scene rather than just giving you a long rundown of everything happening overall.

Jane: Exactly. The authors show that by designing this new task and using SOVCNet, they can get captions that are way more accurate when describing the user's target entity compared to older methods.

Lu: It’s not just about getting better general captions; it’s about making the AI responsive in a way that really matches a specific human interest. That level of precision is where I see a lot of future potential for creative applications, like automated content creation.

Meng: I think the numbers they shared on the accuracy for predicting the subject really back up that claim; it shows they’re hitting those targeted descriptions with solid results on their new test sets.

Lalam: It's about moving beyond just generating text to generating contextually precise text tailored to a specific visual anchor. That’s a big step for how we interact with visual AI systems.

Tom: So, the authors are Chang Teng, Yunchuan Ma, Guorong Li, and others? They’ve introduced this new framework called SOVCNet to solve that problem of getting focused video captions.

Jane: Right. And while they show strong results on those new datasets like SO-MSVD and SO-MSRVTT, they also point out the limits—it’s still tied to that initial user-specified target bounding box.

Lu: The limitation is that it relies heavily on having a good way to define that subject region upfront, which is something we still need to perfect for truly open-ended video understanding.

Meng: So, the immediate implication is a stronger tool for specialized tasks where precision matters more than just high-level summaries.

Lalam: It’s about making AI descriptions much more useful in real-world scenarios where you don't want fluff; you want the exact action or object described.

Tom: That’s what this paper is about—taking general video understanding and making it targeted, precise, and user-driven. Next time we talk, we’ll look at how these new techniques might be applied to generating short video clips from text prompts.

Chang Teng, Yunchuan Ma

School of Computer Science and Technology, Key Lab of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences · Macquarie University

cs.CV

Submitted: 2023-12-20

Updated: 2026-10-03

Importance score: 82/100

The gist: Describing video content according to specific user interests remains a significant challenge in video captioning because existing methods often generate captions that focus on general entities

Key concepts

Subject-Oriented Video Captioning (SOVC): A novel task where users define a specific object or area in a video using a bounding box. The goal is to generate captions that describe the actions and activities of that specific, user-defined subject rather than the entire video content.
Subject-Oriented Sampling Module: This module captures relevant information by clustering video frames based on features derived from both the subject frame and all other frames. It then samples frames within these clusters using cosine similarity to ensure a diverse set of frames directly related to the specified subject.
Subject-Oriented Encoding Module: This component directs the video encoder's attention toward the subject's actions. It combines hard prompts from subject features with patch embeddings and learnable soft prompts, allowing the model to adapt its feature representation specifically to describe what is happening with the target entity.

Terminology

Subject-Oriented Video Captioning — 21× in this paper
Accuracy — 8× in this paper · explained in 1 episode
Adaptation — 7× in this paper · explained in 1 episode
Cosine Similarity — 7× in this paper · explained in 3 episodes
Ablation — 6× in this paper · explained in 1 episode
Ablation study — 5× in this paper · explained in 1 episode
Transformer — 5× in this paper · explained in 2 episodes
Clustering — 4× in this paper · explained in 2 episodes
Representation Learning — 3× in this paper · explained in 2 episodes
Attention Mechanism — 2× in this paper · explained in 2 episodes
Benchmark — 2× in this paper · explained in 2 episodes
Downstream Tasks — 2× in this paper · explained in 2 episodes
Fine-Tuning — 2× in this paper · explained in 5 episodes
Adaptability — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Consistency — 1× in this paper · explained in 3 episodes
Core — 1× in this paper · explained in 2 episodes
Distillation — 1× in this paper · explained in 2 episodes
Knowledge Graph — 1× in this paper · explained in 7 episodes
Sparse Attention — 1× in this paper · explained in 5 episodes
Two-Stage Framework — 1× in this paper · explained in 2 episodes

Summary

Describing video content according to specific user interests remains a significant challenge in video captioning because existing methods often generate captions that focus on general entities rather than a user-specified target, limiting their real-world applicability <ref:2312.13330#pg2>. This paper introduces Subject-Oriented Video Captioning (SOVC), a novel task designed to allow users to specify the describing target via a bounding box, and proposes SOVCNet, a tailored method incorporating subject-oriented sampling and encoding modules to enhance model focus on the subject's activities <ref:2312.13330#pg3>.

The gist: SOVCNet is proposed as a new model for subject-oriented video captioning that utilizes a subject-oriented sampling module to minimize irrelevant information and a subject-oriented encoding module that uses the subject areas as hard prompts and integrates learnable soft prompts to enhance the model’s focus on the subject’s activities and facilitate adaptation to the downstream generation task <ref:2312.13330#pg3>.

How it works

The proposed SOVCNet consists of two key components: a subject-oriented sampling module and a subject-oriented encoding module <ref:2312.13330#pg5>. The objective is to move beyond conventional video captioning, which uniformly samples fixed frames, by developing methods specifically designed to describe the subject’s activities rather than the overall video content <ref:2312.13330#pg6>.

The Subject-Oriented Sampling module addresses the challenge of capturing relevant information by employing a two-step strategy: clustering and sampling <ref:2312.13330#pg7>. This involves first extracting features of frames using Resnet50 to obtain features for the subject frame and the set of all frames, followed by adopting K-means to cluster the video frames into T clusters <ref:2312.13330#pg7>. To ensure diversity, they sample subject-related frames in each cluster based on cosine similarity between the frame features and the subject frame features <ref:2312.13330#pg7>.

The Subject-Oriented Encoding module is designed to direct the video encoder’s attention toward the subject’s activities <ref:2312.13330#pg8>. This module utilizes subject features as hard prompts, in addition to performing patch embedding on video frames, and plugs learnable soft prompts to enable adaptation to the generation task <ref:2312.13330#pg8>. The joint representation of these tokens is then fed into the video encoder to obtain a more appropriate feature representation <ref:2312.13330#pg8>.

Dataset Construction

To support the proposed task, two new datasets, SO-MSVD and SO-MSRVTT, were constructed by re-annotating two widely used general video captioning datasets: MSVD and MSRVTT <ref:2312.13330#pg5>. The annotation process involved four steps:

Using spaCy to extract entities in each caption, which are termed subjects, while discarding captions with abstract subjects like “a video” <ref:2312.13330#pg5>.
Annotating the bounding box in videos for each subject by performing object detection using Faster-RCNN and calculating cosine similarity to rank entity categories against subject words <ref:2312.13330#pg5>.
Manually checking each bounding box for each caption and correcting any missing or incorrect boxes <ref:2312.13330#pg5>.
Reorganizing the dataset into the format of a set denoted as “V, S, C,” where S represents the annotated subject regions within videos, V denotes the video, and C is the captions corresponding to the subject <ref:2312.13330#pg5>.

Experimental Results

Extensive experiments were conducted on SO-MSVD and SO-MSRVTT test sets using four extended baseline models derived from state-of-the-art general video captioning methods, including SwinBERT <ref:2312.13330#pg8>. The results demonstrated that the four state-of-the-art methods encounter a large performance drop on our new task compared to the performance on the original video captioning task <ref:2312.13330#pg9>.

The extension of these baseline models brought significant improvement, with SOVCNet achieving the best performance across all metrics compared to the other four extended methods <ref:2312.13330#pg9>. Specifically, compared to the second-best SwinBert ext, SOVCNet gains 4.0 and 1.1 absolute improvements in terms of the main metric CIDEr on SO-MSRVTT and SO-MSVD, respectively <ref:2312.13330#pg9>. Furthermore, the accuracy of SOVCNet in predicting the subject specified by the user was highest, achieving 65.7% and 63.7% on SO-MSVD and SOMSRVTT <ref:2312.13330#pg9>.

Ablation Study

The ablation study evaluated the effectiveness of the proposed modules and strategies <ref:2312.13330#pg10>. The SOS module made the greatest contribution, increasing the CIDEr score to 59.2 from 57.1 on SO-MSRVTT <ref:2312.13330#pg10>. The cluster-based sampling method achieved the highest CIDEr score of 61.1, exceeding other methods by 1.9 to 9.2 <ref:2312.13330#pg10>. Additionally, the number of soft tokens was explored, showing that 5 is the better choice than other numbers for the CIDEr score <ref:2312.13330#pg10>. Finally, using CLIP V/L-14 features led to better results for all methods on the SO-MSVD dataset in terms of the main metric CIDEr, with improvements ranging from 0.9 to 2.9 <ref:2312.13330#pg10>.

Qualitative Results

Qualitative results showed that SOVCNet better focuses on and accurately describes the entities of interest compared to the origin SwinBERT and extended SwinBERT <ref:2312.13330#pg10>. For instance, in Figure 8 (b), SOVCNet accurately describes it as 'two men are talking' when comparing it to the extended SwinBERT which incorrectly identified two speakers as “a man is talking about a wrestling match” <ref:2312.13330#pg10>. The method was also shown to capture details more effectively, such as describing the “playing guitar” action in Figure 8 (d) when compared to the extended SwinBERT <ref:2312.13330#pg10>.

In conclusion, SOVCNet successfully addresses the challenge of subject-oriented video captioning by proposing a novel task and a method that effectively samples relevant frames and uses subject information as hard prompts alongside learnable soft prompts to generate accurate descriptions of user-specified entities. This approach is validated through extensive quantitative results on new datasets and qualitative demonstrations showing superior focus on the target entity.

REFERENCES

[1] N. Aafaq, N. Akhtar, W. Liu, S. Z. Gilani, and A. Mian, “Spatio-temporal dynamics and semantic attribute enriched visual encoding for video captioning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 12 487–12 496. <ref:2312.13330#pg2>

[5] W. Pei, J. Zhang, X. Wang, L. Ke, X. Shen, and Y.-W. Tai, “Memoryattended recurrent network for video captioning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 8347–8356. <ref:2312.13330#pg2>

[7] C. Sun, A. Myers, C. Vondrick, K. Murphy, and C. Schmid, “Videobert: A joint model for video and language representation learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 7464–7473. <ref:2312.13330#pg2>

[8] S. Zhang, Z. Tan, Z. Zhao, J. Yu, K. Kuang, T. Jiang, J. Zhou, H. Yang, and F Wu “Comprehensive information integration modeling framework for video titling,” in KDD ACM 2020 pp 2744–2754 <ref:2312.13330#pg2>

[9] Y.

Improvements for AI systems

A novel task, Subject-Oriented Video Captioning (SOVC), is introduced, allowing users to specify a target via a bounding box so the generated captions may not focus on the entity that users are particularly interested in be addressed by focusing on user-specified entities.
The SOVCNet model incorporates a subject-oriented sampling module that samples frames related to the subject to minimize irrelevant information and a subject-oriented encoding module that utilizes the subject areas as hard prompts and integrates learnable soft prompts, enhancing the model’s focus on the subject’s activities.
The system can generate highly relevant descriptions by using a mechanism where the encoded tokens and subject tokens are concatenated and then fed into the caption generator for description generation to ensure captions are aligned with the user-specific subjects in the video.
The improved AI system can perform controllable video captioning, enabling users to request specific actions or entities, such as changing a target from a car to a woman, resulting in captions like “A woman is driving a car.”
The framework can minimize irrelevant information by employing a cluster-based sampling strategy where frames are selected based on the cosine similarity between frame features and the subject frame, ensuring that only subject-related video frames are used for description.
The system enhances model focus by applying subject features as hard prompts to direct the video encoder’s attention toward the subject’s activities, allowing it to adapt more effectively to specific downstream generation tasks.
The model's caption generation is improved by using a subject encoder, where the subject features and video features are concatenated as the input of the language generator to ensure generated sentences are directly related to the specified subjects in the video.
The system achieves superior performance by utilizing optimized components; for instance, ablation studies show that The SOS module makes the greatest contribution, increasing the CIDEr score to 59.2 from 57.1 on SO-MSRVTT, demonstrating that frame sampling is crucial for accuracy.