VideoRAG is designed for processing and understanding ultra-long contextual videoRetrieval Augmented Generation (RAG) Framework, developed by the Department of Data Science at the University of Hong Kong. Its core design goal is to address the challenges of efficient analysis and semantic understanding of massive video content.
The system features three main technological innovations:
- Graph-Driven Knowledge Base Architecture: Maintaining semantic consistency across videos by dynamically building a knowledge graph
- Hierarchical multimodal coding: Multimodal features for simultaneous processing of textual and visual content
- Highly efficient processing capabilities: Hundreds of hours of video processing on a single NVIDIA RTX 3090 GPU
Compared to traditional video analytics tools, VideoRAG dramatically improves the retrieval accuracy and relevance of generated answers for long videos by structurally storing video content as a knowledge graph.
This answer comes from the articleVideoRAG: A RAG framework for understanding ultra-long videos with support for multimodal retrieval and knowledge graph constructionThe
