DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
Chinese University of Hong Kong · Shenzhen Institutes of Advanced Technology · +1 more institution
Abstract
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 difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. 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…
Citation impact
- FWCI
- 80.25
- Percentile
- 100%
- References
- 44
Authors
5- ZLZiwei LiuCorresponding
Chinese University of Hong Kong
- PLPing Luo
Shenzhen Institutes of Advanced Technology, Chinese University of Hong Kong
- SQShi Qiu
Group Sense (China)
- XWXiaogang Wang
Shenzhen Institutes of Advanced Technology, Chinese University of Hong Kong
- XTXiaoou Tang
Chinese University of Hong Kong, Shenzhen Institutes of Advanced Technology
Topics & keywords
- Clothing
- Computer science
- Snapshot (computer storage)
- Artificial intelligence
- Pattern recognition (psychology)
- Information retrieval
- Machine learning
- Database