Fashion Image Search via Anchor-Free Detector

Abstract

Clothes image search is the key technique to effectively search the clothes items that are most relevant to the query clothes given by the customer. In this work, we propose an Anchor-free framework for clothes image search by adopting an additional Re-ID branch for similarity learning and global mask branch for instance segmentation. The Re-ID branch is to extract richer feature of target clothes, where we develop a mask pooling layer to aggregate the feature by utilizing the mask of target clothes as the guidance. In this way, the extracted feature will involve more information covered by the mask area of targets instead of only the center point; the global mask branch is to be trained with detection and Re-ID branches simultaneously, where the estimated mask of target clothes can be utilized in reference procedure to guide the feature extraction. Finally, to further enhance the performance of retrieval, we have introduced a match loss to further fine-tune the Re-ID embedding branch in the framework, so that the clothes target can be closer to the same one, while be farther away from different clothes targets. Extensive simulations have been conducted and the results verify the effectiveness of the proposed work.