Summary of Adaptive Video Understanding Agent: Enhancing Efficiency with Dynamic Frame Sampling and Feedback-driven Reasoning, by Sullam Jeoung et al.
Adaptive Video Understanding Agent: Enhancing efficiency with dynamic frame sampling and feedback-driven reasoning
by Sullam Jeoung, Goeric Huybrechts, Bhavana Ganesh, Aram Galstyan, Sravan Bodapati
First submitted to arxiv on: 26 Oct 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 agent-based approach leverages large language models (LLMs) to enhance both the efficiency and effectiveness of long-form video understanding. The method utilizes query-adaptive frame sampling, which processes only relevant frames in real-time, addressing a limitation of existing methods that sample redundant or irrelevant frames. To improve performance while minimizing accessed frames, the approach provides verbal reinforcement to the video-understanding agent using LLMs’ self-reflective capabilities. Evaluations across several benchmarks demonstrate improved state-of-the-art performance and reduced frame sampling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand long videos by using computers that can think like humans. This is done by training special computer models, called large language models (LLMs), to help process only the most important parts of the video in real-time. The system even gives itself advice to improve its performance while saving time and resources. |
