Paper · arXiv 2406.14938
Towards Retrieval Augmented Generation over Large Video Libraries
Yannis Tevissen, Khalil Guetari, Frédéric Petitpont
The proposed system uses Retrieval Augmented Generation (RAG) and large language models to efficiently answer questions about video libraries by generating relevant search queries, retrieving video moments, and producing timestamped responses.
Abstract
Video content creators need efficient tools to repurpose content, a task that often requires complex manual or automated searches. Crafting a new video from large video libraries remains a challenge. In this paper we introduce the task of Video Library Question Answering (VLQA) through an interoperable architecture that applies Retrieval Augmented Generation (RAG) to video libraries. We propose a system that uses large language models (LLMs) to generate search queries, retrieving relevant video moments indexed by speech and visual metadata. An answer generation module then integrates user queries with this metadata to produce responses with specific video timestamps. This approach shows promise in multimedia content retrieval, and AI-assisted video content creation.