Fashion-AttGAN: Attribute-Aware Fashion Editing with Multi-Objective GAN

Autor:	Bing Wu, Qing Ping, Jiangbo Yuan, Wanying Ding
Rok vydání:	2019
Předmět:	FOS: Computer and information sciences business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Task (project management) Domain (software engineering) Face (geometry) ComputerSystemsOrganization_MISCELLANEOUS 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences
Zdroj:	CVPR Workshops
DOI:	10.48550/arxiv.1904.07460
Popis:	In this paper, we introduce attribute-aware fashion-editing, a novel task, to the fashion domain. We re-define the overall objectives in AttGAN and propose the Fashion-AttGAN model for this new task. A dataset is constructed for this task with 14,221 and 22 attributes, which has been made publically available. Experimental results show the effectiveness of our Fashion-AttGAN on fashion editing over the original AttGAN. Comment: 3 pages, 2 figures, accepted at FFSS-USAD workshop @ CVPR 2019
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68be0346e92475c9b1da0d023f063a19 Zobrazit plný text záznamu