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.

翻译：视频片段检索旨在根据自然语言查询定位上下文相关的视频片段，是跨模态定位的关键任务。现有方法侧重于增强所有视频片段与文本描述之间的跨模态交互以理解视频内容。然而，由于时间线上语义分布不均以及视觉背景噪声的存在，持续与所有位置进行交互并不合理。本文提出一种跨模态上下文去噪网络（CDNet），通过解耦复杂关联并去除无关动态信息来实现精确的视频片段检索。具体而言，我们提出查询引导的语义解耦（QSD）机制，通过基于全局和细粒度相关性估计对齐程度来解耦视频片段。同时提出上下文感知动态去噪（CDD）方法，通过学习一组与查询相关的偏移量来增强对已对齐时空细节的理解。在公开基准数据集上的大量实验表明，所提出的CDNet模型取得了最先进的性能。