The goal of video moment retrieval and highlight detection is to identify specific segments and highlights based on a given text query. With the rapid growth of video content and the overlap between these tasks, recent works have addressed both simultaneously. However, they still struggle to fully capture the overall video context, making it challenging to determine which words are most relevant. In this paper, we present a novel Video Context-aware Keyword Attention module that overcomes this limitation by capturing keyword variation within the context of the entire video. To achieve this, we introduce a video context clustering module that provides concise representations of the overall video context, thereby enhancing the understanding of keyword dynamics. Furthermore, we propose a keyword weight detection module with keyword-aware contrastive learning that incorporates keyword information to enhance fine-grained alignment between visual and textual features. Extensive experiments on the QVHighlights, TVSum, and Charades-STA benchmarks demonstrate that our proposed method significantly improves performance in moment retrieval and highlight detection tasks compared to existing approaches. Our code is available at: https://github.com/VisualAIKHU/Keyword-DETR

本研究解决了视频时刻检索和高亮检测中未能充分捕捉整体视频上下文的问题。提出了一种新颖的视频上下文关键词注意力模块，通过视频上下文聚类模块改善关键词的动态理解，从而提升与视觉和文本特征之间的细粒度对齐。实验结果表明，该方法在时刻检索和高亮检测方面显著优于现有方法。

观看视频，捕捉关键词：面向上下文的关键词注意力用于时刻检索和高亮检测