This paper strives to predict fine-grained fashion similarity. In this
similarity paradigm, one should pay more attention to the similarity in terms
of a specific design/attribute between fashion items. For example, whether the
collar designs of the two clothes are similar. It has potential value 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, thus measure the fine-grained
similarity in the corresponding space. The proposed ASEN is comprised of a
global branch and a local branch. The global branch takes the whole image as
input to extract features from a global perspective, while the local branch
takes as input the zoomed-in region-of-interest (RoI) w.r.t. the specified
attribute thus able to extract more fine-grained features. As the global branch
and the local branch extract the features from different perspectives, they are
complementary to each other. Additionally, in each branch, two attention
modules, i.e., Attribute-aware Spatial Attention and Attribute-aware Channel
Attention, are integrated to make ASEN be 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 three fashion-related
datasets, i.e., FashionAI, DARN, and DeepFashion, show the effectiveness of
ASEN for fine-grained fashion similarity prediction and its potential for
fashion reranking. Code and data are available at
this https URL .

本文提出了一种基于 Attribute-Specific Embedding Network (ASEN) 的方法，通过全局和局部特征提取以及注意力机制来计算服装之间的细粒度相似度，进而可应用于多种时尚相关领域，取得了较好的实验结果。

属性特定嵌入学习实现精细服饰相似度预测

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

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.

该论文提出了一种通过 Attribute-Specific Embedding Network 和 Attention modules 来实现细粒度时尚相似度计算的方法，并在四个时尚相关数据集上验证了其有效性和在时尚重新排序方面的潜力。