Product Retrieval (PR) and Grounding (PG), aiming to seek image and object-level products respectively according to a textual query, have attracted great interest recently for better shopping experience. Owing to the lack of relevant datasets, we collect two large-scale benchmark datasets from Taobao Mall and Live domains with about 474k and 101k image-query pairs for PR, and manually annotate the object bounding boxes in each image for PG. As annotating boxes is expensive and time-consuming, we attempt to transfer knowledge from annotated domain to unannotated for PG to achieve un-supervised Domain Adaptation (PG-DA). We propose a {\bf D}omain {\bf A}daptive Produc{\bf t} S{\bf e}eker ({\bf DATE}) framework, regarding PR and PG as Product Seeking problem at different levels, to assist the query {\bf date} the product. Concretely, we first design a semantics-aggregated feature extractor for each modality to obtain concentrated and comprehensive features for following efficient retrieval and fine-grained grounding tasks. Then, we present two cooperative seekers to simultaneously search the image for PR and localize the product for PG. Besides, we devise a domain aligner for PG-DA to alleviate uni-modal marginal and multi-modal conditional distribution shift between source and target domains, and design a pseudo box generator to dynamically select reliable instances and generate bounding boxes for further knowledge transfer. Extensive experiments show that our DATE achieves satisfactory performance in fully-supervised PR, PG and un-supervised PG-DA. Our desensitized datasets will be publicly available here\footnote{\url{https://github.com/Taobao-live/Product-Seeking}}.

翻译：产品检索（PR）与产品定位（PG）旨在根据文本查询分别搜索图像级和对象级产品，为提升购物体验近年引起广泛关注。由于缺乏相关数据集，我们从淘宝商城和直播领域收集了两个大规模基准数据集，分别包含约47.4万和10.1万对图像-查询对用于PR，并手动标注每张图像中的对象边界框用于PG。由于标注边界框成本高且耗时，我们尝试将知识从已标注领域迁移至未标注领域以实现无监督领域自适应（PG-DA）。我们提出**领域自适应产品搜索器**（DATE）框架，将PR和PG视为不同层级的产品搜索问题，以辅助查询与产品的“约会”。具体而言，我们首先为每种模态设计语义聚合特征提取器，以获取集中且全面的特征，用于后续高效检索与细粒度定位任务。随后，我们提出两个协作搜索器，同时搜索图像以完成PR并定位产品以完成PG。此外，我们为PG-DA设计领域对齐器，以缓解源域与目标域之间的单模态边缘分布与多模态条件分布偏移，并设计伪框生成器动态选择可靠实例并生成边界框以进一步传递知识。大量实验表明，我们的DATE在全监督PR、PG和无监督PG-DA任务中均取得了令人满意的性能。脱敏后的数据集将在此公开\footnote{\url{https://github.com/Taobao-live/Product-Seeking}}。