MALS Dataset. [website] We present a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1, 510, 330 image-text pairs, which is about 37.5× larger than prevailing CUHK-PEDES, and all images are annotated with 27 attributes.
University-1652 Dataset. [website] [SoTA] We collect 1652 buildings of 72 universities around the world. University-1652 contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation.
Market-1501 and DukeMTMC-reID Attribute Datasets. [website] We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other.
- 3D Market-1501 Dataset. [website]
- HQ-Market super resolution Dataset. [website]
- DukeMTMC-reID Dataset. [website] [SoTA]
- DukeMTMC-Pose Dataset. [website]
- UTS Person-reID Tutorial. [website]
- Awesome Segmentation Domain Adaptation
- Awesome Vehicle Retrieval
- Awesome Fools
- Awesome Geo-localization
- The illustrated guide to a Ph.D.
- 熊辉: 为什么人前进的路总是被自己挡住
- 陈海波: 一名系统研究者的攀登之路