The paper VideoAgent: Long-form Video Understanding with Large Language Model as Agent presents VideoAgent, a system where a large language model acts as an agent, augmenting long-form video understanding through interactive reasoning and information compilation.

• Utilizes vision-language foundation models for visual information retrieval.
• Achieves high zero-shot accuracy on EgoSchema and NExT-QA benchmarks.
• Conserves computing resources by using fewer frames on average.
• Highlights the potential of agent-based approaches in computer vision.

My Opinion: VideoAgent’s innovative approach can significantly progress how we interact with and analyze long videos, providing an efficient yet effective method for video understanding that could enhance a wide range of multimedia applications.