VideoRAG
V
Videorag
Overview :
VideoRAG is an innovative retrieval-augmented generation framework specifically developed for understanding and processing videos with very long contexts. It intelligently combines graph-driven textual knowledge anchoring with hierarchical multimodal context encoding, enabling comprehension of videos of unrestricted lengths. The framework dynamically builds knowledge graphs, maintains semantic coherence across multiple video contexts, and enhances retrieval efficiency through adaptive multimodal fusion mechanisms. Key advantages of VideoRAG include efficient processing of long-context videos, structured video knowledge indexing, and multimodal retrieval capabilities, allowing it to provide comprehensive answers to complex queries. This framework holds significant technical value and application prospects in the field of long video understanding.
Target Users :
This product is designed for researchers, developers, and professionals in related fields who need to process and understand videos with very long contextual information. This includes video content creators in education, film production teams, and businesses that require knowledge extraction from extensive video libraries. VideoRAG helps them efficiently extract valuable information from lengthy videos, providing robust technical support for video analysis, summarization, and question-answering.
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Use Cases
Researchers can utilize VideoRAG to extract key knowledge points from a wealth of academic lecture videos for academic research and teaching.
Film production teams can leverage VideoRAG to quickly search for video segments related to specific topics, enhancing video editing efficiency.
Businesses can apply VideoRAG to extract critical information from internal training videos for employee training and knowledge management.
Features
Efficient processing of extremely long-context videos: Capable of processing hundreds of hours of video content with a single NVIDIA RTX 3090 GPU.
Structured video knowledge indexing: Distills hundreds of hours of video content into a structured knowledge graph.
Multimodal retrieval: Combines textual semantics with visual content for precise retrieval of relevant video segments.
Support for multilingual video processing: Processes multilingual video content via modifications to the Whisper model.
Provides a long video benchmark dataset: Includes over 160 videos with a total duration exceeding 134 hours, covering a variety of types such as lectures, documentaries, and entertainment.
How to Use
1. Create a Conda environment and install necessary dependencies, including PyTorch and transformers.
2. Download the pre-trained model checkpoints for MiniCPM-V, Whisper, and ImageBind.
3. Provide a list of video file paths to the VideoRAG model for knowledge extraction and indexing.
4. Formulate queries regarding video content; VideoRAG will retrieve and generate responses.
5. Modify the code to support multilingual video processing to accommodate content in different languages.
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