Abstract
Composed video retrieval is a challenging task that strives to retrieve a target video based on a query video and a textual description detailing specific modifications. Standard retrieval frameworks typically struggle to handle the complexity of fine-grained compositional queries and variations in temporal understanding limiting their retrieval ability in the fine-grained setting. To address this issue, we introduce a novel dataset that captures both fine-grained and composed actions across diverse video segments, enabling more detailed compositional changes in retrieved video content. The proposed dataset, named Dense-WebVid-CoVR, consists of 1.6 million samples with dense modification text that is around seven times more than its existing counterpart. We further develop a new model that integrates visual and textual information through Cross-Attention (CA) fusion using grounded text encoder, enabling precise alignment between dense query modifications and target videos. The proposed model achieves state-of-the-art results surpassing existing methods on all metrics. Notably, it achieves 71.3\% Recall@1 in visual+text setting and outperforms the state-of-the-art by 3.4\%, highlighting its efficacy in terms of leveraging detailed video descriptions and dense modification texts. Our proposed dataset, code, and model are available at :this https URL
Abstract (translated)
基于视频组成的检索是一项具有挑战性的任务，旨在根据查询视频和详细描述特定修改的文本描述来检索目标视频。标准的检索框架通常难以处理细粒度组合查询的复杂性和时间理解中的变化，这限制了它们在细粒度设置下的检索能力。为解决这一问题，我们引入了一个新颖的数据集，该数据集捕捉了多样视频片段中细粒度和组成动作的变化，从而使得检索到的视频内容能够进行更详细的组合更改。提出的名为Dense-WebVid-CoVR的数据集包含160万个样本，并且其密集修改文本量是现有同类数据集的七倍左右。 我们进一步开发了一种新模型，通过使用基于接地文本编码器的跨注意力（Cross-Attention, CA）融合方法来整合视觉和文本信息，从而能够精确地对齐密集查询修改与目标视频。所提出的模型在所有度量标准上均取得了超越现有方法的最佳成果。尤其值得注意的是，在视觉+文本设置下，它实现了71.3%的Recall@1，并比最先进的方法高出3.4%，显示了其利用详细视频描述和密集修改文本的有效性。 我们提出的数据集、代码以及模型可在以下链接获得：[提供URL]
URL
https://arxiv.org/abs/2508.14039
