Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video
- URL: http://arxiv.org/abs/2405.08890v1
- Date: Tue, 14 May 2024 18:07:04 GMT
- Title: Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video
- Authors: Tomoya Sugihara, Shuntaro Masuda, Ling Xiao, Toshihiko Yamasaki,
- Abstract summary: This paper proposes a novel self-supervised framework for video summarization guided by Large Language Models (LLMs)
Our model achieves competitive results against other state-of-the-art methods and paves a novel pathway in video summarization.
- Score: 22.60291297308379
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Current video summarization methods primarily depend on supervised computer vision techniques, which demands time-consuming manual annotations. Further, the annotations are always subjective which make this task more challenging. To address these issues, we analyzed the feasibility in transforming the video summarization into a text summary task and leverage Large Language Models (LLMs) to boost video summarization. This paper proposes a novel self-supervised framework for video summarization guided by LLMs. Our method begins by generating captions for video frames, which are then synthesized into text summaries by LLMs. Subsequently, we measure semantic distance between the frame captions and the text summary. It's worth noting that we propose a novel loss function to optimize our model according to the diversity of the video. Finally, the summarized video can be generated by selecting the frames whose captions are similar with the text summary. Our model achieves competitive results against other state-of-the-art methods and paves a novel pathway in video summarization.
Related papers
- MLLM as Video Narrator: Mitigating Modality Imbalance in Video Moment Retrieval [53.417646562344906]
Video Moment Retrieval (VMR) aims to localize a specific temporal segment within an untrimmed long video given a natural language query.
Existing methods often suffer from inadequate training annotations, i.e., the sentence typically matches with a fraction of the prominent video content in the foreground with limited wording diversity.
This intrinsic modality imbalance leaves a considerable portion of visual information remaining unaligned with text.
In this work, we take an MLLM as a video narrator to generate plausible textual descriptions of the video, thereby mitigating the modality imbalance and boosting the temporal localization.
arXiv Detail & Related papers (2024-06-25T18:39:43Z) - HowToCaption: Prompting LLMs to Transform Video Annotations at Scale [77.02631712558251]
We propose to leverage the capability of large language models (LLMs) to obtain fine-grained video descriptions aligned with videos.
We apply our method to the subtitles of the HowTo100M dataset, creating a new large-scale dataset, HowToCaption.
Our evaluation shows that the resulting captions not only significantly improve the performance over many different benchmark datasets for text-video retrieval.
arXiv Detail & Related papers (2023-10-07T19:32:55Z) - VideoXum: Cross-modal Visual and Textural Summarization of Videos [54.0985975755278]
We propose a new joint video and text summarization task.
The goal is to generate both a shortened video clip along with the corresponding textual summary from a long video.
The generated shortened video clip and text narratives should be semantically well aligned.
arXiv Detail & Related papers (2023-03-21T17:51:23Z) - Contrastive Video-Language Learning with Fine-grained Frame Sampling [54.542962813921214]
FineCo is an approach to better learn video and language representations with a fine-grained contrastive objective operating on video frames.
It helps distil a video by selecting the frames that are semantically equivalent to the text, improving cross-modal correspondence.
arXiv Detail & Related papers (2022-10-10T22:48:08Z) - TL;DW? Summarizing Instructional Videos with Task Relevance &
Cross-Modal Saliency [133.75876535332003]
We focus on summarizing instructional videos, an under-explored area of video summarization.
Existing video summarization datasets rely on manual frame-level annotations.
We propose an instructional video summarization network that combines a context-aware temporal video encoder and a segment scoring transformer.
arXiv Detail & Related papers (2022-08-14T04:07:40Z) - CLIP-It! Language-Guided Video Summarization [96.69415453447166]
This work introduces CLIP-It, a single framework for addressing both generic and query-focused video summarization.
We propose a language-guided multimodal transformer that learns to score frames in a video based on their importance relative to one another.
Our model can be extended to the unsupervised setting by training without ground-truth supervision.
arXiv Detail & Related papers (2021-07-01T17:59:27Z) - Open-book Video Captioning with Retrieve-Copy-Generate Network [42.374461018847114]
In this paper, we convert traditional video captioning task into a new paradigm, ie, Open-book Video Captioning.
We propose a novel Retrieve-Copy-Generate network, where a pluggable video-to-text retriever is constructed to retrieve sentences as hints from the training corpus effectively.
Our framework coordinates the conventional retrieval-based methods with orthodox encoder-decoder methods, which can not only draw on the diverse expressions in the retrieved sentences but also generate natural and accurate content of the video.
arXiv Detail & Related papers (2021-03-09T08:17:17Z) - Straight to the Point: Fast-forwarding Videos via Reinforcement Learning
Using Textual Data [1.004766879203303]
We present a novel methodology based on a reinforcement learning formulation to accelerate instructional videos.
Our approach can adaptively select frames that are not relevant to convey the information without creating gaps in the final video.
We propose a novel network, called Visually-guided Document Attention Network (VDAN), able to generate a highly discriminative embedding space.
arXiv Detail & Related papers (2020-03-31T14:07:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.