Summary of Personalized Video Summarization by Multimodal Video Understanding, By Brian Chen et al.
Personalized Video Summarization by Multimodal Video Understanding
by Brian Chen, Xiangyuan Zhao, Yingnan Zhu
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel benchmark and pipeline for user-preferred video summarization, which leverages pre-trained visual language models (VLMs) to avoid the need for large training datasets. The pipeline, called Video Summarization with Language (VSL), takes both video and closed captioning as input and performs semantic analysis at the scene level by converting video frames into text. The user’s genre preference is then used to select relevant textual scenes. Experimental results show that VSL outperforms current state-of-the-art unsupervised video summarization models, with improved adaptability across different datasets compared to supervised query-based video summarization models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video summarization helps users quickly understand and access important information from videos. The goal is to identify the most relevant parts of a video based on user preferences. To make this possible, researchers need large amounts of training data and expensive human labeling. To fix this problem, scientists created a new benchmark for video summarization that captures different user preferences. They also developed a pipeline called Video Summarization with Language (VSL) that uses pre-trained visual language models to avoid needing huge datasets. VSL looks at both the video and closed captions, then analyzes each scene and picks out the most important parts based on the user’s preferred genre. |
Keywords
» Artificial intelligence » Summarization » Supervised » Unsupervised
