Skip to content
master
Switch branches/tags
Go to file
Code

Files

Permalink
Failed to load latest commit information.

README.md

DDAG

Pytorch Code of DDAG for Visible-Infrared Person Re-Identification in ECCV 2020. PDF

Highlight

The goal of this work is to learn a robust and discriminative cross-modality representation for visible-infrarerd person re-identification.

  • Intra-modality Weighted-Part Aggregation (IWPA): It learns discriminative part-aggregated features by mining the contextual part relation.

  • Cross-modality Graph Structured Attention (CGSA): It enhances the feature by incorporating the neighborhood information across two modalities.

Results on the SYSU-MM01 Dataset

Method Datasets Rank@1 mAP mINP
AGW [1] #SYSU-MM01 (All-Search) ~ 47.50% ~ 47.65% ~ 35.30%
DDAG #SYSU-MM01 (All-Search) ~ 54.75% ~ 53.02% ~39.62%
AGW [1] #SYSU-MM01 (Indoor-Search) ~ 54.17% ~ 62.97% ~ 59.23%
DDAG #SYSU-MM01 (Indoor-Search) ~ 61.02% ~ 67.98% ~ 62.61%

*The code has been tested in Python 3.7, PyTorch=1.0. Both of these two datasets may have some fluctuation due to random spliting

1. Prepare the datasets.

  • (1) RegDB Dataset [1]: The RegDB dataset can be downloaded from this website by submitting a copyright form.

    • (Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website).

    • A private download link can be requested via sending me an email (mangye16@gmail.com).

  • (2) SYSU-MM01 Dataset [2]: The SYSU-MM01 dataset can be downloaded from this website.

    • run python pre_process_sysu.py link in to pepare the dataset, the training data will be stored in ".npy" format.

2. Training.

Train a model by

python train_ddag.py --dataset sysu --lr 0.1 --graph --wpa --part 3 --gpu 0
  • --dataset: which dataset "sysu" or "regdb".

  • --lr: initial learning rate.

  • --graph: using graph attention.

  • --wpa: using weighted part attention

  • --part: part number

  • --gpu: which gpu to run.

You may need manually define the data path first.

3. Testing.

Test a model on SYSU-MM01 or RegDB dataset by

python test_ddag.py --dataset sysu --mode all --wpa --graph --gpu 1 --resume 'model_path' 
  • --dataset: which dataset "sysu" or "regdb".

  • --mode: "all" or "indoor" all search or indoor search (only for sysu dataset).

  • --trial: testing trial (only for RegDB dataset).

  • --resume: the saved model path. ** Important **

  • --gpu: which gpu to run.

4. Citation

Please kindly cite the references in your publications if it helps your research:

@inproceedings{eccv20ddag,
  title={Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification},
  author={Ye, Mang and Shen, Jianbing and Crandall, David J. and Shao, Ling and Luo, Jiebo},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020},
}
@article{arxiv20reidsurvey,
  title={Deep Learning for Person Re-identification: A Survey and Outlook},
  author={Ye, Mang and Shen, Jianbing and Lin, Gaojie and Xiang, Tao and Shao, Ling and Hoi, Steven C. H.},
  journal={arXiv preprint arXiv:2001.04193},
  year={2020},
}

Contact: mangye16@gmail.com

About

Pytorch Code of DDAG for Visible-Infrared Person Re-Identification (ECCV20)

Resources

License

Releases

No releases published

Packages

No packages published

Languages