To Become Fashionable: A Brief Review of Outfit Compatibility

被引:1
|
作者
Feng, Ruining [1 ]
机构
[1] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Peoples R China
来源
2020 IEEE CONFERENCE ON TELECOMMUNICATIONS, OPTICS AND COMPUTER SCIENCE (TOCS) | 2020年
关键词
Outfit compatibility; Compatibility algorithms; Recommendation algorithms; Neural Networks; Graph Deep Learning; Collaborative filtering; Compatibility prediction;
D O I
10.1109/TOCS50858.2020.9339690
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
How to dress fashionably has become a major concern for people nowadays. In order to match a fashionable outfit, we need to make our outfit as compatible as possible. Thus, we need to exploit an effective outfit matching scheme and apply it to help people match their own clothes. Outfit compatibility is an emergent computer vision field which tackles this problem. It refers to whether multiple fashion items look good if worn together. Recently, researchers have proposed different compatibility algorithms and recommendation algorithms to make an outfit as compatible as possible and fulfill the users' personal need. In this paper, we present a detailed overview of major compatibility algorithms, such as neural network, and major recommendation algorithms, such as collaborative filtering in outfit compatibility. This paper evaluates the models based on different tasks and datasets and concludes current applications, challenges and possible solutions in outfit compatibility.
引用
收藏
页码:219 / 225
页数:7
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