轻量级视频片段检索系统，提升定位精度与效率
A Lightweight Moment Retrieval System with Global Re-Ranking and Robust Adaptive Bidirectional Temporal Search
- 采用全局重排序与自适应双向时间搜索策略
- 关键帧提取结合图像哈希去重，存储降低显著
- 适合大规模视频库的高效精准检索场景
随着数字视频内容的爆炸式增长，片段级视频检索面临严峻挑战。现有方法在大规模视频语料中定位特定片段时，存在计算效率低、时间上下文捕捉能力弱及内容导航复杂等问题。本文提出一种交互式视频语料片段检索框架，集成超全局重排序（SuperGlobal Reranking）与自适应双向时间搜索（ABTS），有效优化查询相似度、时间稳定性与计算资源消耗。通过使用关键帧提取模型并结合图像哈希去重技术预处理大规模视频语料，本方法在显著降低存储需求的同时，保持了跨多样化视频库的高定位精度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。