Feature Learning with Rank-Based Candidate Selection for Product Search

Nowadays, more and more people buy products via e-commerce websites. We can not only compare prices from different online retailers but also obtain useful review comments from other customers. Especially, people tend to search for visually similar products when they are looking for possible candidates. The need for product search is emerging. To tackle the problem, recent works integrate different additional information (e.g., attributes, image pairs, category) with deep convolutional neural networks (CNNs) for solving cross-domain image retrieval and product search. Based on the state-of-the-art approaches, we propose a rank-based candidate selection for feature learning. Given a query image, we attempt to push hard negative (irrelevant) images away from queries and make ambiguous positive (relevant) images close to queries. We investigate the effects of global and attention-based local features on the proposed method, and achieve 15.8% relative gain for product search.