OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
This addresses the problem of information loss in video understanding for applications such as surveillance and film analysis, representing an incremental improvement over traditional methods.
The paper tackles the challenge of processing extensive videos like 24-hour CCTV footage or full-length films by developing OmAgent, a multi-modal agent framework that efficiently stores and retrieves relevant frames to reduce information loss, with experimental results confirming its efficacy in handling various video types and complex tasks.
Recent advancements in Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding. However, processing extensive videos such as 24-hour CCTV footage or full-length films presents significant challenges due to the vast data and processing demands. Traditional methods, like extracting key frames or converting frames to text, often result in substantial information loss. To address these shortcomings, we develop OmAgent, efficiently stores and retrieves relevant video frames for specific queries, preserving the detailed content of videos. Additionally, it features an Divide-and-Conquer Loop capable of autonomous reasoning, dynamically invoking APIs and tools to enhance query processing and accuracy. This approach ensures robust video understanding, significantly reducing information loss. Experimental results affirm OmAgent's efficacy in handling various types of videos and complex tasks. Moreover, we have endowed it with greater autonomy and a robust tool-calling system, enabling it to accomplish even more intricate tasks.