VideoRAG's multimodal retrieval system represents a cutting-edge technological breakthrough in the current video understanding field. The framework innovatively integrates the dual capabilities of textual semantic analysis and visual content understanding, and realizes cross-modal feature association through advanced models such as ImageBind, which significantly improves the precision and recall of video content retrieval.
The technical implementation of the system is divided into three key levels: in the input phase, the visual features of the video frame and the textual information output from ASR are processed synchronously; in the indexing phase, a multi-level semantic association mapping is constructed; and in the retrieval phase, a hybrid similarity computation method is used to ensure the comprehensiveness of the query results. This design enables VideoRAG to not only recognize keyword-matched scenes, but also understand the deeper semantics of video content, such as emotional expressions and conceptual associations.
Particularly noteworthy is that the framework supports ASR models such as fast-distil-whisper and combines them with visual language models such as MiniCPM-V, demonstrating significantly better performance than unimodal systems when processing professional lecture content and complex narrative scenes.
This answer comes from the articleVideoRAG: A RAG framework for understanding ultra-long videos with support for multimodal retrieval and knowledge graph constructionThe
