GPR+: A Large-Scale Synthetic Dataset for Person Re-identification
Suncheng Xiang1
1 Shanghai Jiao Tong University
For GPR+ (for details of the subset GPR+ please refer to the GPR Homepage) :
| dataset | IDs | boxs | cams | weathers | illumination |
|---|---|---|---|---|---|
| Market-1501 | 1,501 | 32,668 | 6 | - | - |
| DukeMTMC-reID | 1,404 | 36,411 | 8 | - | - |
| CUHK03 | 1,467 | 14,096 | 2 | - | - |
| -------------- | --------- | ------ | ------ | ---------- | -------------- |
| SOMAset | 50 | 100,000 | 250 | - | - |
| SyRI | 100 | 1,680,000 | 100 | - | 140 |
| PersonX | 1,266 | 273,456 | 36 | - | - |
| GPR | 754 | 443,352 | 12 | 7 | 8 |
| GPR+ | 808 | 475,104 | 12 | 7 | 7 |
If you use original GPR dataset for your research, please cite our paper.
@inproceedings{xiang2020unsupervised,
title={Unsupervised Domain Adaptation Through Synthesis For Person Re-Identification},
author={Xiang, Suncheng and Fu, Yuzhuo and You, Guanjie and Liu, Ting},
booktitle={ICME},
pages={1--6},
year={2020},
organization={IEEE}
}
If you use upgraded GPR+ dataset for your research, please cite our paper.
@inproceedings{xiang2021taking,
title={Taking A Closer Look at Synthesis: Fine-Grained Attribute Analysis for Person Re-Identification},
author={Xiang, Suncheng and Fu, Yuzhuo and You, Guanjie and Liu, Ting},
booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={3765--3769},
year={2021},
organization={IEEE}
}
We sincerely thank the outstanding annotation team for their excellent work. This work is partially supported by the National Natural Science Foundation of China under Project(Grant No.61977045) and SJTU-SMARCHIT Joint Laboratory of Smart Building.
For further questions and suggestions about our datasets and methods, please feel free to contact Suncheng Xiang: [email protected]