Attribute-and-Identity Correspondence Network for Clothes Search

Clothes search is a challenging task, because the uncontrolled capture condition and massive styles of clothing images make the accurate retrieval difficult. To address the problem, we design a novel Attribute-and-Identity Correspondence Network (AICN) to obtain more robust features for clothes retrieval. The attribute information of clothes includes category, color, shape and so forth. The images of the same clothing pictured under different pose or background conditions have the same identity. Specifically, the AICN is comprised of two sub-branches: the attribute one and the identity one, and the two branches share the low level features. The former exploits attribute information to learn the discriminative feature, while the latter is to handle the variations of images with the same identity. Furthermore, we design a compactness loss to force the feature spaces of the two branches to become close, since their semantic spaces have overlap intuitively. Experiments on the benchmark and practical dataset demonstrate the effectiveness and priority of our method.