Abstract
The rapid growth of video content demands efficient and precise retrieval systems. While vision-language models (VLMs) excel in representation learning, they often struggle with adaptive, time-sensitive video retrieval. This paper introduces a novel framework that combines vector similarity search with graph-based data structures. By leveraging VLM embeddings for initial retrieval and modeling contextual relationships among video segments, our approach enables adaptive query refinement and improves retrieval accuracy. Experiments demonstrate its precision, scalability, and robustness, offering an effective solution for interactive video retrieval in dynamic environments.
Abstract (translated)
视频内容的快速增长要求高效的检索系统。虽然视觉-语言模型（VLM）在表示学习方面表现出色，但它们通常难以处理适应性强、时效性高的视频检索需求。本文介绍了一种结合向量相似度搜索和基于图的数据结构的新框架。通过利用VLM嵌入进行初始检索，并建模视频片段之间的上下文关系，我们的方法能够实现自适应查询细化并提高检索准确性。实验结果证明了该方法在精度、可扩展性和鲁棒性方面的有效性，为动态环境中的交互式视频检索提供了一种有效解决方案。
URL
https://arxiv.org/abs/2503.17415
