@article{01bd33f9e07c473ea442cacce2a24e84,
title = "Interpretable partitioned embedding for intelligent multi-item fashion outfit composition",
abstract = "Intelligent fashion outfit composition has become more popular in recent years. Some deep-learning-based approaches reveal competitive composition. However, the uninterpretable characteristic makes such a deep-learning-based approach fail to meet the businesses', designers', and consumers' urges to comprehend the importance of different attributes in an outfit composition. To realize interpretable and intelligent multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network contains two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit-composition graph and an attribute matching map. Extensive experiments demonstrate that (1) the partitioned embedding have unmingled parts that correspond to different attributes and (2) outfits recommended by our model are more desirable in comparison with the existing methods.",
keywords = "Adversarial, Embedding, Interpretable, Outfit composition",
author = "Zunlei Feng and Zhenyun Yu and Yongcheng Jing and Sai Wu and Mingli Song and Yezhou Yang and Junxiao Jiang",
note = "Funding Information: This work is supported by the National Key Research and Development Program (2016YFB1200203), National Natural Science Foundation of China (61572428, U1509206, 61872315, 61661146001), Program of International Science and Technology Cooperation under Grant (2013DFG12840), and Key Research and Development Program of Zhejiang Province (2018-C01004). Authors{\textquoteright} addresses: Z. Feng, Z. Yu, Y. Jing, S. Wu, and M. Song (corresponding author), Zhejiang University, China; emails: {zunleifeng, zhenyunyu, ycjing, wusai, brooksong}@zju.edu.cn; Y. Yang, Arizona State University; email: yz.yang@asu.edu; J. Jiang, Alibaba Group, China; email: junxiao.jjx@alibaba-inc.com. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. {\textcopyright} 2019 Association for Computing Machinery. 1551-6857/2019/07-ART61 $15.00 https://doi.org/10.1145/3326332 Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.",
year = "2019",
month = aug,
doi = "10.1145/3326332",
language = "English (US)",
volume = "15",
journal = "ACM Transactions on Multimedia Computing, Communications and Applications",
issn = "1551-6857",
publisher = "Association for Computing Machinery (ACM)",
number = "2s",
}