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	DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations
	Ziwei Liu; Ping Luo; Shi Qiu; Xiaogang Wang; Xiaoou Tang
	2016
会议名称	CVPR2016
会议地点	美国
英文摘要	Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are diffi- cult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion 1 , a large-scale clothes dataset with comprehensive annota- tions. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and land- marks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effective- ness of FashionNet and the usefulness of DeepFashion.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10021]
专题	深圳先进技术研究院_集成所
作者单位	2016
推荐引用方式 GB/T 7714	Ziwei Liu,Ping Luo,Shi Qiu,et al. DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations[C]. 见:CVPR2016. 美国.

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