Summary of Videoagent: Long-form Video Understanding with Large Language Model As Agent, by Xiaohan Wang et al.
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
by Xiaohan Wang, Yuhui Zhang, Orr Zohar, Serena Yeung-Levy
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 Medium Difficulty summary: The paper addresses the challenge of understanding long-form videos by developing a novel agent-based system called VideoAgent. This system uses a large language model as a central agent to iteratively identify and compile crucial information to answer a question, leveraging vision-language foundation models to translate and retrieve visual information. The approach is motivated by human cognitive processes for long-form video understanding, emphasizing interactive reasoning and planning over processing lengthy visual inputs. VideoAgent is evaluated on the EgoSchema and NExT-QA benchmarks, achieving 54.1% and 71.3% zero-shot accuracy with only 8.4 and 8.2 frames used on average, respectively. These results demonstrate superior effectiveness and efficiency of VideoAgent compared to current state-of-the-art methods, highlighting its potential in advancing long-form video understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper tries to understand long videos by creating a new system called VideoAgent. It’s like a super smart helper that looks at the video and figures out what it’s about. Instead of trying to watch the whole video, VideoAgent works by looking at small parts and putting together what’s important. The system uses big language models and computer vision tools to make this happen. In tests on two hard challenges, VideoAgent did much better than other methods that try to do the same thing. |
Keywords
» Artificial intelligence » Large language model » Zero shot
