Summary of Vca: Video Curious Agent For Long Video Understanding, by Zeyuan Yang et al.
VCA: Video Curious Agent for Long Video Understanding
by Zeyuan Yang, Delin Chen, Xueyang Yu, Maohao Shen, Chuang Gan
First submitted to arxiv on: 12 Dec 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 The proposed paper introduces a curiosity-driven video agent with self-exploration capability, dubbed as VCA, which efficiently builds a comprehensive understanding of complex video sequences using vision language models (VLMs). Unlike existing methods that rely on sampling numerous frames or incorporating auxiliary tools using large language models (LLMs), VCA employs a tree-search structure to explore video segments and collect frames. This approach leverages the self-generated intrinsic reward from VLMs to guide its exploration, enabling it to capture the most crucial information for reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VCA is a new way to understand long videos without needing lots of computing power. Instead of looking at every frame, it uses a special search method to find the important parts. It gets clues from what it sees and learns more by exploring the video on its own. This helps it understand complex videos better than other methods that need help from humans. |
