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arXiv · 2002.02814

Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network

Zhe Ma·Jianfeng Dong·Yao Zhang·Zhongzi Long·Yuan He·Hui Xue·Shouling Ji
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Abstract

This paper strives to learn fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute among fashion items, which has potential values in many fashion related applications such as fashion copyright protection. To this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space. With two attention modules, i.e., Attribute-aware Spatial Attention and Attribute-aware Channel Attention, ASEN is able to locate the related regions and capture the essential patterns under the guidance of the specified attribute, thus make the learned attribute-specific embeddings better reflect the fine-grained similarity. Extensive experiments on four fashion-related datasets show the effectiveness of ASEN for fine-grained fashion similarity learning and its potential for fashion reranking.

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Zhe Ma, Jianfeng Dong, Yao Zhang, Zhongzi Long, Yuan He, Hui Xue, Shouling Ji. 2020-02-07. Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network. https://arxiv.org/abs/2002.02814

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