Snap And Find: Deep Discrete Cross-domain Garment Image Retrieval

Abstract

With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-scale product databases to find their desired items. However, it is challenging for conventional retrieval systems to match up the item photos captured by customers and the ones officially released by stores, especially for garment images. To bridge the customer- and store- provided garment photos, existing studies have been widely exploiting the clothing attributes (\textit\{e.g.,\} black) and landmarks (\textit\{e.g.,\} collar) to learn a common embedding space for garment representations. Unfortunately they omit the sequential correlation of attributes and consume large quantity of human labors to label the landmarks. In this paper, we propose a deep multi-task cross-domain hashing termed \textit\{DMCH\}, in which cross-domain embedding and sequential attribute learning are modeled simultaneously. Sequential

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• arxiv keyluo2019snap

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