CVPR 2023 - DATE: Domain Adaptive Product Seeker for E-commerce

In this episode we discuss DATE: Domain Adaptive Product Seeker for E-commerce by Haoyuan Li, Hao Jiang, Tao Jin, Mengyan Li, Yan Chen, Zhijie Lin, Yang Zhao, Zhou Zhao. The paper presents a framework for Product Retrieval (PR) and Grounding (PG) that can seek image and object-level products respectively according to a textual query to aid in better shopping experience. The authors collected two benchmark datasets from Taobao Mall and Live domains with about 474k and 101k image-query pairs for PR, and manually annotated the object bounding boxes in each image for PG. They propose a Domain Adaptive Product Seeker (DATE) framework that can perform un-supervised Domain Adaptation (PG-DA) by transferring knowledge from annotated to unannotated domains. The DATE achieved satisfactory performance in fully-supervised PR, PG and un-supervised PG-DA.