@inproceedings{2d02847750244964bd39186675fa778c,
title = "Coarse-to-Fine Attribute Editing for Fashion Images",
abstract = "With the development of Generative Adversarial Networks, attribute editing has been more and more popular in computer vision. The previous works employed the multi-domain image-to-image translation framework to solve attribute editing. However, it is difficult to generate consistent texture with the original images. Meanwhile, the un-target attribute regions are changed by using these methods. To address these problems, this paper presents a coarse-to-fine attribute editing scheme (CFAE) for fashion images. CFAE is composed of coarse stage and refine stage. In the coarse stage, we design a landmark-based attention scheme in conjunction with StarGAN [5] to locate and edit the target attribute regions. Subsequently, DeepFillv2 [16] is employed in the refine stage to make the target attribute regions consistent with the original image. In experiment section, we compare our method with several state-of-the-art methods on OUTFIT Dataset and the results demonstrate the effectiveness of CFAE.",
keywords = "Attribute editing, Deep learning, Fashion, GAN",
author = "Qinghu Wang and Jianjun Qian and Xingxing Zou and Jian Yang and Waikeung Wong",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 1st CAAI International Conference on Artificial Intelligence, CICAI 2021 ; Conference date: 05-06-2021 Through 06-06-2021",
year = "2021",
doi = "10.1007/978-3-030-93046-2\_34",
language = "English",
isbn = "9783030930455",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "396--407",
editor = "Lu Fang and Yiran Chen and Guangtao Zhai and Jane Wang and Ruiping Wang and Weisheng Dong",
booktitle = "Artificial Intelligence - 1st CAAI International Conference, CICAI 2021, Proceedings",
address = "Germany",
}