Summary of Irag: Advancing Rag For Videos with An Incremental Approach, by Md Adnan Arefeen et al.
iRAG: Advancing RAG for Videos with an Incremental Approach
by Md Adnan Arefeen, Biplob Debnath, Md Yusuf Sarwar Uddin, Srimat Chakradhar
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 This paper tackles the challenge of using retrieval-augmented generation (RAG) systems for understanding videos. RAG combines language generation and information retrieval strengths, powering applications like chatbots. However, applying RAG to video understanding faces two significant limitations: high processing times required for upfront text description conversion of large video corpora, and limited information capture in these descriptions. The absence of prior user queries further complicates developing a system for interactive querying of video data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to understand videos better. Right now, we can’t easily ask questions about videos like we do with text or images. One problem is that it takes a long time to convert all the information in a large collection of videos into text descriptions. Another issue is that not everything important from the video gets recorded in these descriptions. The goal is to create a system where we can ask questions and get answers about videos without knowing what questions people will ask ahead of time. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
