Awesome-Cross-Domain-Person-Re-identification

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

Awesome-Cross-Domain-Person-Re-identification

★ 20 Open GitHub ↗

Awesome-Cross-Domain-Person-Re-identification

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Awesome-Cross-Domain-Person-Re-identification[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

![CrossDomain](https://github.com/Yimin-Liu/Awesome-Cross-Domain-Person-Re-identification/blob/main/CrossDomain.png)

![DomainGap](https://github.com/Yimin-Liu/Awesome-Cross-Domain-Person-Re-identification/blob/main/DomainGap.png)

## KeyWords

Transfer learning, Cross dataset, Unsupervised, Person Reidentification

## Datasets

- Awesome re-id dataset [[github](https://github.com/NEU-Gou/awesome-reid-dataset)]
- Market-1501 Leaderboard [[page](https://jingdongwang2017.github.io/Projects/ReID/Datasets/result_market1501.html)]
- Duke Leaderboard [[page](https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/leaderboard)]
- Re-id dataset collection [[page](http://robustsystems.coe.neu.edu/sites/robustsystems.coe.neu.edu/files/systems/projectpages/reiddataset.html)]

## Benchmarks

- [Duke to Market](https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-duke-to)
- [Market to Duke](https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-market-to)
- [Market to MSMT](https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-market-to-1)
- [Duke to MSMT](https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-duke-to-1)

## Methods

1. Unsupervised clustering
2. Image-style transfer 
3. Unsupervised domain transfer

## Paper list

### Year 2020

1. Zou, Yang, et al. "Joint disentangling and adaptation for cross-domain person re-identification." *arXiv preprint arXiv:2007.10315* (2020)..[[Paper](https://arxiv.org/pdf/2007.10315.pdf)]

2. Jin, Xin, et al. "Global Distance-distributions Separation for Unsupervised Person Re-identification." *arXiv preprint arXiv:2006.00752* (2020).[[Paper](https://arxiv.org/pdf/2006.00752.pdf)]

3. Ge, Yixiao, Dapeng Chen, and Hongsheng Li. "Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification." *arXiv preprint arXiv:2001.01526* (2020).[[Paper](https://arxiv.org/pdf/2001.01526)] [[Code](https://github.com/yxgeee/MMT)]

4. Zhang, Xinyu, et al. "Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory." *arXiv preprint arXiv:2001.04123* (2020).[[Paper](https://arxiv.org/pdf/2001.04123.pdf)]

5. Yang, Fengxiang, et al. "Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification." *AAAI*. 2020.[[Paper](https://arxiv.org/pdf/1912.01349)]

6. Li, Jianing, and Shiliang Zhang. "Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification." *European Conference on Computer Vision*. Springer, Cham, 2020.[[Paper](https://arxiv.org/pdf/2007.10854.pdf)]

### Year 2019

1. Wu, Jinlin, et al. "Clustering and dynamic sampling based unsupervised domain adaptation for person re-identification." *2019 IEEE International Conference on Multimedia and Expo (ICME)*. IEEE, 2019.[[Paper](http://www.cbsr.ia.ac.cn/users/zlei/papers/JLWU-ICME-2019.pdf)]

2. Chen, Chao, et al. "Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation." *Proceedings of the AAAI Conference on Artificial Intelligence*. Vol. 33. 2019.[[Paper](https://www.aaai.org/ojs/index.php/AAAI/article/download/4202/4080)]

3. Zhu, Xiangping, Pietro Morerio, and Vittorio Murino. "Unsupervised Domain-Adaptive Person Re-Identification Based on Attributes." *2019 IEEE International Conference on Image Processing (ICIP)*. IEEE, 2019.[[Paper](https://arxiv.org/pdf/1908.10359)]

4. Zhou, Shuren, Maolin Ke, and Peng Luo. "Multi-camera transfer GAN for person re-identification." *Journal of Visual Communication and Image Representation* 59 (2019): 393-400.[[Paper](https://www.sciencedirect.com/science/article/pii/S1047320319300379)]

5. Chen, Yanbei, Xiatian Zhu, and Shaogang Gong. "Instance-guided context rendering for cross-domain person re-identification." *Proceedings of the IEEE International Conference on Computer Vision*. 2019.[[Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Instance-Guided_Context_Rendering_for_Cross-Domain_Person_Re-Identification_ICCV_2019_paper.pdf)]

6. Zhang, Xinyu, et al. "Self-training with progressive augmentation for unsupervised cross-domain person re-identification." *Proceedings of the IEEE International Conference on Computer Vision*. 2019.[[Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Self-Training_With_Progressive_Augmentation_for_Unsupervised_Cross-Domain_Person_Re-Identification_ICCV_2019_paper.pdf)] [[Code](https://github.com/zhangxinyu-xyz/PAST-ReID)]

7. Fu, Yang, et al. "Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification." *Proceedings of the IEEE International Conference on Computer Vision*. 2019.[[Paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf)] [[Code](https://github.com/SHI-Labs/Self-Similarity-Grouping)]

8. Liu, Jiawei, et al. "Adaptive transfer network for cross-domain person re-identification." *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*. 2019.[[Paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Adaptive_Transfer_Network_for_Cross-Domain_Person_Re-Identification_CVPR_2019_paper.pdf)]

9. Li, Yu-Jhe, et al. "Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation." *Proceedings of the IEEE International Conference on Computer Vision*. 2019.[[Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Cross-Dataset_Person_Re-Identification_via_Unsupervised_Pose_Disentanglement_and_Adaptation_ICCV_2019_paper.pdf)]

10. Zhang, Chengyuan, Lin Wu, and Yang Wang. "Crossing generative adversarial networks for cross-view person re-identification." *Neurocomputing* 340 (2019): 259-269.[[Paper](https://arxiv.org/pdf/1801.01760.pdf)]

11. Yu, Hong Xing , et al. "Unsupervised Person Re-Identification by Soft Multilabel Learning." *2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)* IEEE, 2019.[[Paper](https://arxiv.org/abs/1903.06325)] [[Code](https://github.com/KovenYu/MAR)]

### Year 2018

1. Fan, Hehe, et al. "Unsupervised person re-identification: Clustering and fine-tuning." *ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)* 14.4 (2018): 1-18.[[Paper](https://arxiv.org/pdf/1705.10444)] [[Code](https://github.com/hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning)]

2. Li, Yu-Jhe, et al. "Adaptation and re-identification network: An unsupervised deep transfer learning approach to person re-identification." *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops*. 2018.[[Paper](https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w6/Li_Adaptation_and_Re-Identification_CVPR_2018_paper.pdf)]

3. Wei, Longhui, et al. "Person transfer gan to bridge domain gap for person re-identification." *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*. 2018.[[Paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Person_Transfer_GAN_CVPR_2018_paper.pdf)] [[Code]()]

4. Wang, Jingya, et al. "Transferable joint attribute-identity deep learning for unsupervised person re-identification." *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*. 2018.[[Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Transferable_Joint_Attribute-Identity_CVPR_2018_paper.pdf)]

5. Zhong, Zhun, et al. "Camera style adaptation for person re-identification." *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*. 2018.[[Paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhong_Camera_Style_Adaptation_CVPR_2018_paper.pdf)] [[Code](https://github.com/zhunzhong07/CamStyle)]

6. Choi, Yunjey, et al. "Stargan: Unified generative adversarial networks for multi-domain image-to-image translation." *Proceedings of the IEEE conference on computer vision and pattern recognition*. 2018.[[Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_StarGAN_Unified_Generative_CVPR_2018_paper.pdf)] [[Code](https://github.com/cosmic119/StarGAN)]

7. Huang, Houjing, et al. "Enhancing alignment for cross-domain person reidentification." *arXiv preprint arXiv:1812.11369* (2018).[[Paper](https://arxiv.org/pdf/1812.11369.pdf)] [[Code](https://github.com/huanghoujing/EANet)]

8. Lv, Jianming, et al. "Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns." *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*. 2018.[[Paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Lv_Unsupervised_Cross-Dataset_Person_CVPR_2018_paper.pdf)] [[Code](https://github.com/ahangchen/TFusion)]

9. Lin, Shan, et al. "Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification." *arXiv preprint arXiv:1807.01440* (2018).[[Paper](https://arxiv.org/pdf/1807.01440)]

### Year 2017

1. Long, Mingsheng, et al. "Deep transfer learning with joint adaptation networks." *International conference on machine learning*. PMLR, 2017.[[Paper](http://proceedings.mlr.press/v70/long17a/long17a.pdf)]

2. Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." *Proceedings of the IEEE international conference on computer vision*. 2017.[[Paper](https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.pdf)] [[Code](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)]

3. Zheng, Zhedong, Liang Zheng, and Yi Yang. "Unlabeled samples generated by gan improve the person re-identification baseline in vitro." *Proceedings of the IEEE International Conference on Computer Vision*. 2017.[[Paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zheng_Unlabeled_Samples_Generated_ICCV_2017_paper.pdf)]

4. Wei, Xiu-Shen, et al. "Selective convolutional descriptor aggregation for fine-grained image retrieval." *IEEE Transactions on Image Processing* 26.6 (2017): 2868-2881.[[Paper](https://arxiv.org/pdf/1604.04994)]

## Contributing

Please help contribute this list by contacting [me](iminliu@yeah.net) or add [pull request](https://github.com/Yimin-Liu/Awesome-Cross-Domain-Person-Re-identification/pulls)

Markdown format:
```markdown
- References. 
  [[pdf]](link) 
  [[code]](link)
```

Awesome-Cross-Domain-Person-Re-identification

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README