期刊
NEURAL COMPUTING & APPLICATIONS
卷 32, 期 9, 页码 4519-4530出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3691-y
关键词
Generative adversarial network; Clothing segmentation; Clothing retrieval; Clothing retrieval; Video advertising
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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