Cross-Video Semantic Consistency Assurance Program
VideoRAG uses graph knowledge base technology to solve the semantic consistency challenge:
- Dynamic mapping::
- Entity Relationship Networking through Neo4j
- Real-time reasoning to fill in missing associations
- Event chain modeling in the time dimension
- Layered processing mechanism::
- Low-level: frame-level feature extraction
- Middle layer: scenario semantic parsing
- High level: cross video thematic correlation
- Elements of implementation::
- Properly configure neo4j connection parameters
- Perform regular map optimization (OPTIMIZE)
- Setting up a reasonable cache-elimination policy
- Consistency calibration methods::
- Designing semantic distance thresholds
- Implementing Conflict Detection Rules
- Establishment of a closed loop of manual feedback
Extended proposal: it can be combined with LLM for graph quality assessment, and use RPC calls to realize distributed graph services to deal with ultra-large scale data.
This answer comes from the articleVideoRAG: A RAG framework for understanding ultra-long videos with support for multimodal retrieval and knowledge graph constructionThe
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