OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer (2406.16620v3)

Abstract: Recent advancements in 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.

• The paper presents OmAgent, a novel framework that integrates retrieval-augmented generation with a divide-and-conquer loop to process extensive video data effectively.
• It employs a specialized 'rewinder' tool and video2RAG preprocessor to minimize information loss and maintain comprehensive detail extraction.
• Experimental results on a benchmark of 2000+ Q&A pairs demonstrate improved event localization, accuracy, and potential for real-world video analysis applications.

Insights into OmAgent: A Multi-modal Agent Framework for Complex Video Understanding

The paper presents OmAgent, a novel multi-modal agent framework designed to address complex video understanding tasks, particularly in processing extensive video formats such as 24-hour CCTV footage or full-length films. Recognizing the inherent challenges posed by the enormous volume of data and the substantial information loss that traditional key frame extraction methods introduce, this research aims to mitigate such shortcomings through a more comprehensive and robust approach.

Key Contributions and Methodologies

The paper outlines several pivotal contributions made by OmAgent to the field of video understanding:

1. Integration of Multimodal Retrieval-Augmented Generation (RAG): OmAgent incorporates a video2RAG preprocessor to effectively store high-level information extracted from videos, allowing for detailed knowledge extraction without the extensive information loss typically experienced in traditional methods. This ensures a richer and more informative retrieval process for further querying and analysis.
2. Divide-and-Conquer Loop (DnC Loop): Central to OmAgent's architecture is the DnC Loop, which provides an autonomous framework capable of dividing complex tasks into manageable subtasks. This loop iteratively simplifies tasks until they can be directly addressed, enhancing both efficiency and accuracy in handling intricate video-related queries. Notably, this framework is designed to autonomously invoke APIs and employ external tools to further improve accuracy and task execution.
3. The Rewinder Tool: Within the DnC Loop, a specialized tool called the "rewinder" is developed, allowing OmAgent to revisit and review specific segments of a video, thus emulating a human-like ability to process and recall detailed visual information. This tool is instrumental in preserving continuity and ensuring comprehensive understanding, particularly in scenarios where minute details are of essence.
4. Novel Benchmark for Video Understanding: To measure OmAgent’s video comprehension capabilities, the authors propose a new benchmark dataset encompassing over 2000 question-answer pairs. This dataset serves to evaluate how effectively the agent handles complex video understanding tasks, with results suggesting notable advancements over existing methodologies.

Evaluation and Performance

OmAgent's efficacy is substantiated by experimental evaluations on tasks encompassing video reasoning, event localization, information summarization, and incorporation of external knowledge. The comprehensive evaluation framework outlined in the paper emphasizes an in-depth analysis of both qualitative and quantitative performance metrics.

• General Problem-Solving Tests: Using benchmarks such as MBPP and FreshQA, OmAgent demonstrates superior general problem-solving skills. Compared to prominent existing frameworks like XAgent, OmAgent yields higher accuracy rates, particularly due to its task planning and fault-tolerance features powered by the Rescuer mechanism.
• Long-form Video Understanding: On the new benchmark, OmAgent performed robustly across varied question types, notably improving the quality of event localization and external knowledge incorporation compared to other baseline methods. This performance reinforces the system's potential for deployment in real-world applications requiring nuanced and comprehensive video data analysis.

Implications and Future Directions

The introduction of OmAgent posits significant implications in both practical applications and theoretical paradigms of video understanding. From surveillance operations to cinematic content analysis, the enhanced ability to manage and extract meaningful insights from voluminous video data could revolutionize several industries.

Looking forward, the authors identify potential areas for further development, including refining the accuracy of time-sensitive event localizations and addressing challenges related to character recognition in complex video narratives. Integrating advanced image processing algorithms and improving audio-visual synchronization are contemplated future enhancements.

In summary, OmAgent represents a substantial step forward in the field of video understanding, challenging existing paradigms and laying a foundation for further exploration and innovation within the field of AI-driven video analysis.