Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification

We provide the code for reproducing result of our paper Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification.

Getting Started

Dataset Preparation

CUHK-PEDES

Organize them in dataset folder as follows:

|-- dataset/
|   |-- <CUHK-PEDES>/
|       |-- imgs
            |-- cam_a
            |-- cam_b
            |-- ...
|       |-- reid_raw.json

Download the CUHK-PEDES dataset from here and then run the process_CUHK_data.py as follow:

cd SSAN
python ./dataset/process_CUHK_data.py

ICFG-PEDES

Organize them in dataset folder as follows:
```
|-- dataset/
|   |-- <ICFG-PEDES>/
|       |-- imgs
            |-- test
            |-- train 
|       |-- ICFG_PEDES.json
```
Note that our ICFG-PEDES is collect from MSMT17 and thus we keep its storage structure in order to avoid the loss of information such as camera label, shooting time, etc. Therefore, the file testand train here are not the way ICFG-PEDES is divided. The exact division of ICFG-PEDES is determined by ICFG-PDES.json. The ICFG-PDES.json is organized like the reid_raw.json in CUHK-PEDES .

Please request the ICFG-PEDES database from 272521211@qq.com and then run the process_ICFG_data.py as follow:
```
cd SSAN
python ./dataset/process_ICFG_data.py
```

Training and Testing

sh experiments/CUHK-PEDES/train.sh 
sh experiments/ICFG-PEDES/train.sh

Evaluation

sh experiments/CUHK-PEDES/test.sh 
sh experiments/ICFG-PEDES/test.sh

Results on CUHK-PEDES and ICFG-PEDES

Our Results on CUHK-PEDES dataset

Our Results on ICFG-PEDES dataset

Citation

If this work is helpful for your research, please cite our work:

@article{ding2021semantically,
  title={Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification},
  author={Ding, Zefeng and Ding, Changxing and Shao, Zhiyin and Tao, Dacheng},
  journal={arXiv preprint arXiv:2107.12666},
  year={2021}
}

zifyloo/SSAN