Spatial Constraint Multiple Granularity Attention Network For Clothesretrieval

Clothes retrieval is very popular on e-commerce websites, and it is still a very challenging problem. These difficulties come from clothing deformation, variations in angle, noisy background and so on. According to our observation, although the overall shape of clothing varies greatly, there is slightly deformation in some local parts (such as collar, pocket, etc.) which has a significant impact on the final retrieval performance. In this paper, we propose an end-to-end attention-based deep network. It consists of two main branches, one of which is used to extract the key parts in clothing from different granularities. The other is designed to simulate the spatial contextual between different parts to obtain more discriminative feature representation by means of Long Short-Term Memory (LSTM). Experimental results demonstrate that our method is superior to previous methods in terms of performance and effectiveness.