Long-form video understanding is significantly advanced by the introduction of VideoAgent, an agent-based system involving Large Language Models (LLMs) as central agents. Deploying vision-language foundation models as tools, VideoAgent iteratively compiles essential information from visual inputs to answer complex questions. Evaluated against challenging benchmarks, it demonstrates high zero-shot accuracies while utilizing a minimal number of frames.

• The system conceptually mimics human interactive reasoning and planning over handling extended visual sequences.
• It delivers a new level of efficiency, marked by a considerable reduction in the number of frames required.
• VideoAgent’s agent-based methodology sets new standards over current methods for comprehension tasks.
• This form of AI agent underscores the potential in long-form video understanding, with broad implications for future research and applications.

This paper underlines the significance of adopting agent-based architectures in machine perception, pushing the boundaries of computer vision without relying on extensive visual data processing. This strategic model fosters innovation within AI, inviting exploration in various areas, from robotics to interactive media analysis. Read More