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Neural Computing and Applications

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17.08.2018 | Original Article

ClothingOut: a category-supervised GAN model for clothing segmentation and retrieval

verfasst von: Haijun Zhang, Yanfang Sun, Linlin Liu, Xinghao Wang, Liuwu Li, Wenyin Liu

Erschienen in: Neural Computing and Applications | Ausgabe 9/2020

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Abstract

This paper presents a new framework, ClothingOut, which utilizes generative adversarial network (GAN) to generate tiled clothing images automatically. Specifically, we design a novel category-supervised GAN model by learning transformation rules between clothes on wearers and clothes that are tiled. Our method features in adding category attribute to a traditional GAN model. For model training, we built a large-scale dataset containing over 20,000 pairs of wearer images and their corresponding tiled clothing images. The learned model can be straightforwardly applied to video advertising and cross-scenario clothing image retrieval. We evaluated our generated images which can be regarded as the segmentation from the wearer images from two aspects: authenticity and retrieval performance. Experimental results demonstrate the effectiveness of our method.

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Titel: ClothingOut: a category-supervised GAN model for clothing segmentation and retrieval
verfasst von: Haijun Zhang
Yanfang Sun
Linlin Liu
Xinghao Wang
Liuwu Li
Wenyin Liu
Publikationsdatum: 17.08.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 9/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3691-y

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