Fine-Grained Fashion Representation Learning by Online Deep Clustering

Yang Jiao, Ning Xie, Yan Gao, Chien-chih Wang, Yi Sun ;

"Fashion designs are rich in visual details associated with various visual attributes at both global and local levels. As a result, effective modeling and analyzing fashion requires fine-grained representations for individual attributes. In this work, we present a deep learning based online clustering method to jointly learn fine-grained fashion representations for all attributes at both instance and cluster level, where the attribute-specific cluster centers are online estimated. Based on the similarity between fine-grained representations and cluster centers, attribute-specific embedding spaces are further segmented into class-specific embedding spaces for fine-grained fashion retrieval. To better regulate the learning process, we design a three-stage learning scheme, to progressively incorporate different supervisions at both instance and cluster level, from both original and augmented data, and with ground-truth and pseudo labels. Experiments on FashionAI and DARN datasets in retrieval task demonstrated the efficacy of our method compared with competing baselines."