Multi-Modal Retrieval Augmented Visual Understanding and Generation

摘要

This paper presents a doctoral research focusing on integrating Retrieval-Augmented Generation (RAG) into video-related multimodal tasks. Existing RAG studies predominantly target text, images, or tabular data, overlooking the unique value of video as a knowledge carrier. We address this gap by: 1) proposing AdaVideoRAG, a framework that adaptively allocates retrieval strategies based on query complexity for long-video understanding; 2) developing REViG (RAG-Enhanced Video Generation) to optimize prompt engineering via retrieved knowledge for controllable video synthesis; 3) constructing the UltraVideo dataset (UHD-4K/8K resolution, 100+ themes, 10 structured captions per video) and HiVU/HiVG benchmarks to evaluate RAG-driven video tasks. Experiments validate the effectiveness of our methods, and we outline future plans to unify video understanding and generation through Agentic RAG for AGI-oriented research.