Abstract
The exponential growth of digital video content has posed critical challenges in moment-level video retrieval, where existing methodologies struggle to efficiently localize specific segments within an expansive video corpus. Current retrieval systems are constrained by computational inefficiencies, temporal context limitations, and the intrinsic complexity of navigating video content. In this paper, we address these limitations through a novel Interactive Video Corpus Moment Retrieval framework that integrates a SuperGlobal Reranking mechanism and Adaptive Bidirectional Temporal Search (ABTS), strategically optimizing query similarity, temporal stability, and computational resources. By preprocessing a large corpus of videos using a keyframe extraction model and deduplication technique through image hashing, our approach provides a scalable solution that significantly reduces storage requirements while maintaining high localization precision across diverse video repositories.
Abstract (translated)
数字视频内容的指数级增长为在大规模视频库中检索特定片段（即时刻级别视频检索）带来了重大挑战。现有的方法难以高效地定位这些片段，特别是在处理庞大的视频数据集时。当前的检索系统受到计算效率低下、时间上下文限制以及导航视频内容固有复杂性的制约。 本文提出了一种新的交互式视频库时刻检索框架，该框架结合了超级全局重排序机制和自适应双向时间搜索（ABTS），在查询相似性、时间稳定性及计算资源之间进行策略性优化。通过使用关键帧提取模型对大量视频数据集进行预处理，并采用图像哈希技术去除重复内容，我们的方法提供了一种可扩展的解决方案，显著减少了存储需求，同时在多种视频库中保持了高精度定位。 这种方法不仅提高了检索效率和准确性，而且还为用户提供了更丰富的交互体验。
URL
https://arxiv.org/abs/2504.09298
