解耦与去噪：解决视频时刻检索中的上下文错位问题
Disentangle and denoise: Tackling context misalignment for video moment retrieval
AI总结：
针对视频时刻检索中上下文错位问题，提出含查询引导语义解耦与上下文感知动态去噪的CDNet，在公开基准上取得了最优性能。
摘要：
视频时刻检索旨在根据自然语言查询定位上下文内的视频片段，是跨模态定位的核心任务。现有方法侧重增强所有视频时刻与文本描述的跨模态交互以实现视频理解，但由于时间线上语义分布不均且视觉背景存在噪声，持续与所有位置交互并不合理。本文提出跨模态上下文去噪网络（CDNet），通过解耦复杂关联、去除无关动态变化实现精准时刻检索。具体而言，提出查询引导的语义解耦模块（QSD），通过全局与细粒度相关性估计对齐程度，实现视频时刻的解耦；还提出上下文感知动态去噪模块（CDD），通过学习一组与查询相关的偏移量，增强对对齐时空细节的理解。在公开基准上的大量实验表明，所提CDNet取得了当前最优性能。
英文摘要：
Video Moment Retrieval, which aims to locate in-context video moments according to a natural language query, is an essential task for cross-modal grounding. Existing methods focus on enhancing the cross-modal interactions between all moments and the textual description for video understanding. However, constantly interacting with all locations is unreasonable because of uneven semantic distribution across the timeline and noisy visual backgrounds. This paper proposes a cross-modal Context Denoising Network (CDNet) for accurate moment retrieval by disentangling complex correlations and denoising irrelevant dynamics.Specifically, we propose a query-guided semantic disentanglement (QSD) to decouple video moments by estimating alignment levels according to the global and fine-grained correlation. A Context-aware Dynamic Denoisement (CDD) is proposed to enhance understanding of aligned spatial-temporal details by learning a group of query-relevant offsets. Extensive experiments on public benchmarks demonstrate that the proposed CDNet achieves state-of-the-art performances.
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。
