Enhancing Subsequent Video Retrieval via Vision-Language Models (VLMs)
This work addresses efficient video retrieval for users in dynamic environments, representing an incremental improvement over existing VLM-based methods.
The paper tackled the problem of adaptive, time-sensitive video retrieval by introducing a framework that combines vector similarity search with graph-based data structures, resulting in improved retrieval accuracy as demonstrated in experiments.
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.