An efficient framework for outfit compatibility prediction towards occasion

Author:

Vo Anh H.,Le Tung B. T.,Pham Huy V.,Nguyen Bao T.ORCID

Abstract

AbstractNowadays, in a communicating society, fashion is an integral part of a human life, and it is more comfortable and confident when people dress well. Outfit compatibility is not only a combination of different items but also regarding various aspects, such as style, user preferences, and specific occasions. Most of the existing works lead to address the outfit compatibility concerning only style or user preferences, and have no regard for occasions. In this paper, we propose an efficient method for both outfit compatibility and the fill-in-the-blank tasks according to specific occasions. To this end, we utilized an auxiliary classification branch to learn the significantly important features regarding specific occasions. Besides, a sequence to sequence approach is also applied to learn the relationship of different items along with a visual semantic space, which is able to learn the connection between visual features and their semantic presentation. To demonstrate the effectiveness of the proposed method, we conduct experiments on our newly collected Shoplook-Occasion dataset. The experimental results indicate that our proposed method improved the AUC metric from 0.02 to 0.15% and from 0.5 to 4% on accuracy, compared with other approaches for outfit compatibility problem conditioning on specific occasions.

Funder

Royal Melbourne Institute of Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Revolutionizing Fashion Recommendations: A Deep Dive into Deep Learning-based Recommender Systems;Proceedings of the 7th International Conference on Networking, Intelligent Systems and Security;2024-04-18

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3