Composed Image Retrieval (CoIR) has recently gained popularity as a task that considers both text and image queries together, to search for relevant images in a database. Most CoIR approaches require manually annotated datasets, comprising image-text-image triplets, where the text describes a modification from the query image to the target image. However, manual curation of CoIR triplets is expensive and prevents scalability. In this work, we instead propose a scalable automatic dataset creation methodology that generates triplets given video-caption pairs, while also expanding the scope of the task to include composed video retrieval (CoVR). To this end, we mine paired videos with a similar caption from a large database, and leverage a large language model to generate the corresponding modification text. Applying this methodology to the extensive WebVid2M collection, we automatically construct our WebVid-CoVR dataset, resulting in 1.6 million triplets. Moreover, we introduce a new benchmark for CoVR with a manually annotated evaluation set, along with baseline results. Our experiments further demonstrate that training a CoVR model on our dataset effectively transfers to CoIR, leading to improved state-of-the-art performance in the zero-shot setup on both the CIRR and FashionIQ benchmarks. Our code, datasets, and models are publicly available at https://imagine.enpc.fr/~ventural/covr.

翻译：组合图像检索（CoIR）通过同时考虑文本和图像查询，在数据库中搜索相关图像的任务近期受到广泛关注。大多数CoIR方法依赖人工标注的数据集，包含图像-文本-图像三元组，其中文本描述从查询图像到目标图像的修改。然而，人工标注CoIR三元组成本高昂且难以扩展。本研究提出一种可扩展的自动数据集创建方法，利用视频-字幕对生成三元组，同时将任务范围扩展至组合视频检索（CoVR）。为此，我们从大规模数据库中挖掘具有相似字幕的配对视频，并借助大型语言模型生成对应的修改文本。将该方法应用于大规模WebVid2M数据集，我们自动构建了包含160万三元组的WebVid-CoVR数据集。此外，我们引入了基于人工标注评估集的CoVR新基准及基线结果。实验表明，在该数据集上训练的CoVR模型可有效迁移至CoIR任务，在零样本设置下显著提升CIRR和FashionIQ基准的最优性能。我们的代码、数据集和模型已开源：https://imagine.enpc.fr/~ventural/covr。