Comprehending long-form videos presents significant challenges necessitating models that can reason across extended multi-modal sequences. The novel agent-based system, VideoAgent, brings a large language model into play as a central agent to selectively compile pivotal information for question-answering, with vision-language foundation models translating and retrieving visual details.

Exhibiting zero-shot accuracy of 54.1% and 71.3% on the EgoSchema and NExT-QA benchmarks respectively, VideoAgent demonstrates its method’s efficacy and efficiency, outstripping current state-of-the-art approaches. The essence of VideoAgent lies in its interactive reasoning, akin to human cognitive processes, enhancing the depth of video understanding. Here are the highlights:

• Pioneering the use of a central language model as an agent for long-form videos.
• Leveraging minimal frames for maximal information extraction and efficient understanding.
• Surpassing existing methods in both effectiveness and computational thriftiness.
• Forging a path for advanced long-form video analysis and intelligent content contextualization.

VideoAgent’s significance stems from its potential to shift the paradigm in how we interact with visual content. By streamlining the cognitive workflow of video understanding, it sets new standards for accuracy and efficiency. Looking ahead, broadening the scope of VideoAgent to encompass diverse video types and complexities promises advancements in areas like automated content moderation and smart video summarization. Discover more about VideoAgent’s innovative approach to long-form video understanding.