A solution for efficiently processing ultra-long videos
To efficiently process hundreds of hours of video content, VideoRAG offers the following implementation-specific path:
- Hardware Optimization: Uses NVIDIA RTX 3090 GPUs as the base computing unit and accelerates parallel computing through CUDA.
- hierarchical coding technique: A hierarchical multimodal contextual coding architecture is used to divide the video into
- Time Dimension Slicing Process
- Spatial dimension feature extraction
- Semantic level correlation analysis
- knowledge graph construction: Dynamically establishing video semantic associations through a graph-driven textual knowledge base, realizing the
- De-redundant information compression
- Cross-fragment semantic association
- Real-time update mechanism
- Hands-on advice: Pay attention to version matching when installing, especially
- PyTorch video processing-specific branch
- Version-specific DECORD decoding libraries
- Specially optimized whisper speech recognition model
Supplementary solution: for larger datasets, consider splitting the processing task into multiple GPUs for parallel execution and utilizing the Neo4j graph database for distributed storage.
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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