Summary of Video-rag: Visually-aligned Retrieval-augmented Long Video Comprehension, by Yongdong Luo et al.
Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
by Yongdong Luo, Xiawu Zheng, Xiao Yang, Guilin Li, Haojia Lin, Jinfa Huang, Jiayi Ji, Fei Chao, Jiebo Luo, Rongrong Ji
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes Video Retrieval-Augmented Generation (Video-RAG), a novel approach to improve long video understanding by leveraging visually-aligned auxiliary texts. The method extracts relevant information from pure video data, such as audio, optical character recognition, and object detection, and incorporates it into an existing large video-language model (LVLM) as additional context. This plug-and-play solution offers significant performance gains across various benchmarks, outperforming proprietary models like Gemini-1.5-Pro and GPT-4o when used with a 72B model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding long videos by giving them extra information to work with. The problem is that current computer models can only understand short videos or small pieces of text, so the researchers came up with a new way to give them more context. They take existing video data and extract useful bits like audio, text, and object recognition, then add it to the original video frames. This makes the computer model much better at understanding long videos, and even beats proprietary models! |
Keywords
» Artificial intelligence » Gemini » Gpt » Language model » Object detection » Rag » Retrieval augmented generation
