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 https://github.com/maryeon/asenpp .
翻译:本文力求预测细微细微时装相似性。 在这个相似的范例中, 人们应该更加关注时装项目之间特定设计/ 属性的相似性。 例如, 两件服装的项圈设计是否相似。 它在许多时装相关应用程序中具有潜在价值, 如时装版权保护。 为此, 我们提议一个属性特有嵌入网络( SASN), 以共同学习多个属性特定的嵌入, 从而测量相应空间的精细细微相似性。 拟议的ASEN 包括一个全球分支和一个地方分支。 全球分支将整张时装作为从全球角度提取特征的输入, 而本地分支则将两张图像作为类似时装格式的输入。 而本地分支则将缩放区域( ROI) ( w.r. t. ) 指定属性可以提取更精细的特性。 随着全球分支和本地分支从不同角度提取这些特性, 它们彼此互补。 此外, 在每个分支中, 两个关注模块, 即 属性感应具有精确的空间关注度和属性识别方式, 以类似时尚的直径直径直径显示的图像, 因此, 将显示直径直径直径直径直径数据 。 。 将显示与直径直径的直径的直映系数据 。