DATE: Domain Adaptive Product Seeker for E-commerce
Haoyuan Li, Hao Jiang, Tao Jin, Mengyan Li, Yan Chen, Zhijie Lin, Yang, Zhao, Zhou Zhao

TL;DR
The paper introduces DATE, a framework for product retrieval and grounding in e-commerce that leverages domain adaptation to improve performance with limited annotations, using large-scale datasets and novel techniques.
Contribution
We propose a domain adaptive product seeker framework that unifies retrieval and grounding tasks with domain adaptation and pseudo-labeling for unannotated data.
Findings
Achieves high performance in fully-supervised product retrieval and grounding.
Effectively transfers knowledge to un-supervised domain adaptation setting.
Provides large-scale benchmark datasets for future research.
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques · Domain Adaptation and Few-Shot Learning
