RVAA: Recursive Vision-Action Agent for Long Video Understanding (github.com)

A new implementation called Recursive Vision-Action Agent (RVAA) has been introduced for long video understanding, based on the Recursive Language Model (RLM) paradigm proposed in a recent paper. Unlike traditional approaches that attempt to fit entire videos into a single context window, RVAA treats video content as an external environment, employing mechanisms like temporal slicing and recursive sub-models to manage the complexity of long videos, which can exceed 38,000 frames in length. This innovative system allows the agent to explore video content programmatically, capturing local semantic information and synthesizing it into a coherent global understanding. The significance of RVAA lies in its ability to overcome the limitations faced by conventional large language models (LLMs) in processing extensive visual data. By systematically segmenting videos and using specialized models for localized analysis, RVAA minimizes issues like context fragmentation and information overload while maintaining cost efficiency. During evaluations, RVAA demonstrated high accuracy in topic extraction from a 21-minute news broadcast, identifying themes through a structured multi-step reasoning process. Technical innovations include a REPL-based interaction for exploring video content and a vision-language capability powered by the Llama 3.2 model, which facilitates the conversion of visual data into text for LLM processing. This approach could set a new standard for AI applications in video analysis and understanding.