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 the retrieval task demonstrated the efficacy of our method compared with competing baselines.
CITATION STYLE
Jiao, Y., Xie, N., Gao, Y., Wang, C. chih, & Sun, Y. (2022). Fine-Grained Fashion Representation Learning by Online Deep Clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13687 LNCS, pp. 19–35). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19812-0_2
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