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


  • Zhe Ma Zhejiang University
  • Jianfeng Dong Zhejiang Gongshang University
  • Zhongzi Long Zhejiang University
  • Yao Zhang Zhejiang University
  • Yuan He Alibaba Group
  • Hui Xue Alibaba Group
  • Shouling Ji Zhejiang University



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. Code and data are available at




How to Cite

Ma, Z., Dong, J., Long, Z., Zhang, Y., He, Y., Xue, H., & Ji, S. (2020). Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11741-11748.



AAAI Technical Track: Vision