Summary of Video Enriched Retrieval Augmented Generation Using Aligned Video Captions, by Kevin Dela Rosa
Video Enriched Retrieval Augmented Generation Using Aligned Video Captions
by Kevin Dela Rosa
First submitted to arxiv on: 27 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
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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 proposed “aligned visual captions” integrate video information into retrieval augmented generation (RAG) based chat assistant systems, facilitating the use of large language models (LLMs). These textual captions describe video content, requiring less multimedia input than traditional methods. By adapting captions to specific use cases and curating a dataset with automatic evaluation procedures for RAG tasks, this work aims to advance progress in multimodal LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to combine videos with chatbots. It creates “visual captions” that describe what’s happening in the video, making it easier for computers to understand and use the information. This can help make chatbots more useful by giving them access to more types of media. The authors also created a dataset and ways to test how well this works on different tasks. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
