Summary of Towards Retrieval Augmented Generation Over Large Video Libraries, by Yannis Tevissen et al.
Towards Retrieval Augmented Generation over Large Video Libraries
by Yannis Tevissen, Khalil Guetari, Frédéric Petitpont
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 introduces the task of Video Library Question Answering (VLQA), which aims to efficiently repurpose video content by querying large video libraries. The authors propose a system that uses large language models (LLMs) to generate search queries, retrieving relevant video moments indexed by speech and visual metadata. This system shows promise in multimedia content retrieval and AI-assisted video content creation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video creators need tools to reuse videos efficiently. A new paper introduces the task of finding specific moments in large video libraries. The authors create a system that uses big language models to search for relevant parts of videos, based on audio and visual clues. This system is useful for searching and reusing video content. |
Keywords
» Artificial intelligence » Question answering
