yimin-liu/awesome-cross-domain-person-re-identification

Awesome-Cross-Domain-Person-Re-identification

Awesome-Cross-Domain-Person-Re-identification

KeyWords

Transfer learning, Cross dataset, Unsupervised, Person Reidentification

Datasets

• Awesome re-id dataset [github]
• Market-1501 Leaderboard [page]
• Duke Leaderboard [page]
• Re-id dataset collection [page]

Benchmarks

• Duke to Market
• Market to Duke
• Market to MSMT
• Duke to MSMT

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]

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

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] [Code]

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]

5. Yang, Fengxiang, et al. "Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification." AAAI. 2020.[Paper]

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]

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]

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]

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]

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]

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]

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] [Code]

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] [Code]

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]

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]

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

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] [Code]

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] [Code]

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]

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] [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]

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] [Code]

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] [Code]

7. Huang, Houjing, et al. "Enhancing alignment for cross-domain person reidentification." arXiv preprint arXiv:1812.11369 (2018).[Paper] [Code]

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] [Code]

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]

Year 2017

1. Long, Mingsheng, et al. "Deep transfer learning with joint adaptation networks." International conference on machine learning. PMLR, 2017.[Paper]

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] [Code]

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]

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]

Contributing

Please help contribute this list by contacting me or add pull request

Markdown format:

- References. 
  [[pdf]](link) 
  [[code]](link)