DDAG

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

A Huawei MindSpore implementation of our DDAG method is HERE. Thanks to Zhiwei Zhang [email protected].

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 ([email protected]).

• (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: [email protected]