  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.
