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

Autor:	Ma, Zhe, Dong, Jianfeng, Zhang, Yao, Long, Zhongzi, He, Yuan, Xue, Hui, Ji, Shouling
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 16 pages, 13 figutes. Accepted by AAAI 2020. Code and data are available at https://github.com/Maryeon/asen
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.02814 Zobrazit plný text záznamu View this record from Arxiv