面向多模态RAG检索的视频帧采样方法的实证比较
An Empirical Comparison of Video Frame Sampling Methods for Multi-Modal RAG Retrieval
AI总结：
本文通过实证比较多种视频帧采样方法在Video RAG检索中的召回率与存储效率，发现采样策略可在降低存储和处理成本的同时达到与存储全部帧相当或更优的recall@k表现，为实际系统设计提供依据。
摘要：
文献中详述的众多视频帧采样方法，在没有并排对比分析的情况下，给确定Video RAG模式的最佳视频帧方法带来了重大挑战。在本工作中，我们研究了使用自然语言问题进行视频与帧检索时帧采样方法的权衡。我们探索了采样帧数量与检索召回分数之间的平衡，旨在识别高效的视频帧采样策略，以在降低存储和处理需求的同时保持较高的检索效能。我们的研究聚焦于Video RAG模式所需的向量数据库内图像数据（视频帧）的存储与检索，比较了各种帧采样技术的有效性。我们的调查表明，对于本工作所涵盖的多种方法，在文本到视频和文本到帧检索任务上的recall@k指标与存储视频中每一帧的方法相当或更优。我们的发现旨在为实际Video RAG实现中的帧采样方法选择提供参考，并作为该领域创新研究的起点。
英文摘要：
Numerous video frame sampling methodologies detailed in the literature present a significant challenge in determining the optimal video frame method for Video RAG pattern without a comparative side-by-side analysis. In this work, we investigate the trade-offs in frame sampling methods for Video & Frame Retrieval using natural language questions. We explore the balance between the quantity of sampled frames and the retrieval recall score, aiming to identify efficient video frame sampling strategies that maintain high retrieval efficacy with reduced storage and processing demands. Our study focuses on the storage and retrieval of image data (video frames) within a vector database required by Video RAG pattern, comparing the effectiveness of various frame sampling techniques. Our investigation indicates that the recall@k metric for both text-to-video and text-to-frame retrieval tasks using various methods covered as part of this work is comparable to or exceeds that of storing each frame from the video. Our findings are intended to inform the selection of frame sampling methods for practical Video RAG implementations, serving as a springboard for innovative research in this domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
