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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 zhangzw12319@163.com.
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The goal of this work is to learn a robust and discriminative cross-modality representation for visible-infrarerd person re-identification.
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Intra-modality Weighted-Part Aggregation (IWPA): It learns discriminative part-aggregated features by mining the contextual part relation.
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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.
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(1) RegDB Dataset [1]: The RegDB dataset can be downloaded from this website by submitting a copyright form.
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(Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website).
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A private download link can be requested via sending me an email (mangye16@gmail.com).
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(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.
- run
2. Training.
Train a model by
python train_ddag.py --dataset sysu --lr 0.1 --graph --wpa --part 3 --gpu 0
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--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'
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--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