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Awesome-Unsupervised-Person-Re-identification

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Awesome-Unsupervised-Person-Re-identificationAwesome

Unsupervised person re-identification is a computer vision task that involves identifying and matching individuals across different non-overlapping cameras or video frames without using any manual annotations or labeled data. The goal is to find the same person or object in different camera views or time instances, regardless of changes in lighting, pose, clothing, or camera viewpoints.

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KeyWords

Unsupervised Person Reidentification,Tranfer Learning,Domain Adaptation,Clustering

Table of Contents

Datasets

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

Methods

Benchmarks

UDA re-ID

Pure re-ID bechmarks

Paper list

Add papers published in ACMMM2023

  • Rethinking Pseudo-Label-Based Unsupervised Person Re-ID with Hierarchical Prototype-Based Graph[Paper]

Add papers published in ICME2023

  • Camera Proxy based Contrastive Learning with Hard Sampling for Unsupervised Person Re-identification[Paper]

Add papers published in ICCV2023

  • Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification[Paper]
  • Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification[Paper]
  • Camera-Driven Representation Learning for Unsupervised Domain Adaptive Person Re-identification[Paper]

Add papers published in CVPR2023

  • Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning[Paper]

Add papers published in CVPR2022

  • Part-based Pseudo Label Refinement for Unsupervised Person Re-identification[Paper][Code]
  • Implicit Sample Extension for Unsupervised Person Re-Identification[Paper]

Add papers published in AAAI2022

  • Divide-and-Regroup Clustering for Domain Adaptive Person Re-Identification
  • Multi-Centroid Representation Network for Domain Adaptive Person Re-ID[Paper]
  • SECRET: Self-Consistent Pseudo Label Refinement for Unsupervised Domain Adaptive Person Re-Identification
  • Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification[Paper]
  • Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-Identification[Paper]
  • Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption[Paper]
  • Reliability Exploration with Self-Ensemble Learning for Domain Adaptive Person Re-Identification

Add papers published in ICCV2021

  • Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-Identification[Paper]
  • ICE: Inter-Instance Contrastive Encoding for Unsupervised Person Re-Identification[Paper]
  • Meta Pairwise Relationship Distillation for Unsupervised Person Re-Identification[Paper]
  • Towards Discriminative Representation Learning for Unsupervised Person Re-Identification[Paper]
  • IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID[Paper]

Add some journal articles

  • Attribute-Aligned Domain-Invariant Feature Learning for Unsupervised Domain Adaptation Person Re-Identiļ¬cation(TIFS2021)
  • Complementary Pseudo Labels for Unsupervised Domain Adaptation On Person Re-Identiļ¬cation(TIP2021)
  • Domain Adaptive Person Re-Identiļ¬cation via Camera Style Generation and Label Propagation(TIFS2020)
  • Dynamic Graph Co-Matching for Unsupervised Video-Based Person Re-Identiļ¬cation(TIP2019)
  • End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identiļ¬cation(TIFS2021)
  • Hierarchical Connectivity-Centered Clustering for Unsupervised Domain Adaptation on Person Re-Identiļ¬cation(TIP2021)
  • Homogeneous-to-Heterogeneous: Unsupervised Learning for RGB-Infrared Person Re-Identiļ¬cation(TIP2021)
  • Unsupervised Domain Adaptation with Background Shift Mitigating for Person Re-Identiļ¬cation(IJCV2021)
  • Leveraging Virtual and Real Person for Unsupervised Person Re-Identiļ¬cation(TMM2020)
  • Part-aware Progressive Unsupervised Domain Adaptation for Person Re-Identiļ¬cation(TMM2021)
  • Progressive Unsupervised Person Re-Identiļ¬cation by Tracklet Association With Spatio-Temporal Regularization(TMM2021)
  • Self-Supervised Agent Learning for Unsupervised Cross-Domain Person Re-Identiļ¬cation(TIP2020)
  • Self-Training With Progressive Representation Enhancement for Unsupervised Cross-Domain Person Re-Identiļ¬cation(TIP2021)
  • Unsupervised Cross Domain Person Re-Identiļ¬cation by Multi-Loss Optimization Learning(TIP2021)
  • Unsupervised Person Re-identiļ¬cation via Cross-Camera Similarity Exploration(TIP2020)

The latest paper released in 2021

  • Refining Pseudo Labels With Clustering Consensus Over Generations for Unsupervised Object Re-Identification[Paper](CVPR2021)

  • Unsupervised Pre-Training for Person Re-Identification[Paper](CVPR2021)

  • Unsupervised Multi-Source Domain Adaptation for Person Re-Identification[Paper](CVPR2021)

  • Refining Pseudo Labels With Clustering Consensus Over Generations for Unsupervised Object Re-Identification[Paper](CVPR2021)

  • Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification[Paper](CVPR2021)

  • Joint Generative and Contrastive Learning for Unsupervised Person Re-Identification[Paper](CVPR2021)

  • Intra-Inter Camera Similarity for Unsupervised Person Re-Identification[Paper](CVPR2021)

  • Group-aware Label Transfer for Domain Adaptive Person Re-identification[Paper](CVPR2021)

  • Camera-Aware Proxies for Unsupervised Person Re-Identification[Paper](AAAI2021)

  • Unsupervised Domain Adaptation for Person Re-Identification via Heterogeneous Graph Alignment[Paper](AAAI2021)

  • Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification[Paper](AAAI2021)

Unsupervised Domain Adaptation

Domain style transfer or Data Augmentation

  • Li, Yu-Jhe, et al. "Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.[Paper]

  • Ge Y, Zhu F, Chen D, et al. "Self-paced contrastive learning with hybrid memory for domain adaptive object re-id". Advances in Neural Information Processing Systems, 2020, 33: 11309-11321.[Paper]

  • Liu, Jiawei, et al. "Adaptive transfer network for cross-domain person re-identification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.[Paper]

  • Zhong, Zhun, et al. "Camstyle: A novel data augmentation method for person re-identification." IEEE Transactions on Image Processing 28.3 (2018): 1176-1190.[Paper]

  • Zhong, Zhun, et al. "Generalizing a person retrieval model hetero-and homogeneously." Proceedings of the European Conference on Computer Vision (ECCV). 2018.[Paper]

  • Zhong, Zhun, et al. "Camera style adaptation for person re-identification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.[Paper]

  • 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]

  • Qian, Xuelin, et al. "Pose-normalized image generation for person re-identification." Proceedings of the European conference on computer vision (ECCV). 2018.[Paper]

  • Deng, Weijian, et al. "Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.[Paper]

  • Chen, Yanbei, Xiatian Zhu, and Shaogang Gong. "Instance-guided context rendering for cross-domain person re-identification." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.[Paper]

Representation learning based

  • Huang, Yangru, et al. "Domain adaptive attention model for unsupervised cross-domain person re-identification." arXiv preprint arXiv:1905.10529 (2019).[Paper]

  • Qi, Lei, et al. A novel unsupervised camera-aware domain adaptation framework for person re-identification." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.[Paper]

  • Jin, Xin, et al. "Style normalization and restitution for generalizable person re-identification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.[Paper]

  • Zhong, Zhun, et al. "Invariance matters: Exemplar memory for domain adaptive person re-identification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.[Paper][Code]

  • Bak, Slawomir, Peter Carr, and Jean-Francois Lalonde. "Domain adaptation through synthesis for unsupervised person re-identification." Proceedings of the European Conference on Computer Vision (ECCV). 2018.[Paper]

  • 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]

  • 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]

  • 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]

  • 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]

  • Geng, Mengyue, et al. "Deep transfer learning for person re-identification." arXiv preprint arXiv:1611.05244 (2016).[Paper]

  • Peng, Peixi, et al. "Unsupervised cross-dataset transfer learning for person re-identification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.[Paper]//ļ¼Ÿ

  • Ma, Andy J., et al. "Cross-domain person reidentification using domain adaptation ranking svms." IEEE transactions on image processing 24.5 (2015): 1599-1613.[Paper]

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

  • Zhang, Xinyu, et al. "Self-training with progressive augmentation for unsupervised cross-domain person re-identification." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.[Paper]//ļ¼Ÿ

  • Wu, Jinlin, et al. "Unsupervised graph association for person re-identification." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.[Paper][Code]

  • 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]

Target domain clustering

  • Yu, Hong-Xing, Ancong Wu, and Wei-Shi Zheng. "Cross-view asymmetric metric learning for unsupervised person re-identification." Proceedings of the IEEE international conference on computer vision. 2017.[Paper]

  • 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]

  • Fu, Yang, et al. "Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.[Paper]

  • Yunpeng Zhai, Shijian Lu, Qixiang Ye, Xuebo Shan, Jie Chen, Rongrong Ji, and Yonghong Tian. Ad-cluster: Augmented discriminative clustering for domain adaptive person re-identification. In CVPR, 2020. 1, 3, 8 [Paper]

  • Zhao, Fang, et al. "Unsupervised domain adaptation with noise resistible mutual-training for person re-identification." European Conference on Computer Vision. Springer, Cham, 2020.[Paper]

  • 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]

  • Yang, Fengxiang, et al. "Asymmetric co-teaching for unsupervised cross-domain person re-identification." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 07. 2020.[Paper]

  • 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]

  • 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]

  • Zhang, Minying, et al. "Unsupervised Domain Adaptation for Person Re-identification via Heterogeneous Graph Alignment." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 4. 2021.[Paper](AAAI2021)

  • Zheng, Kecheng, et al. "Group-aware label transfer for domain adaptive person re-identification." (CVPR2021)[Paper]

  • Jin, Xin, et al. "Global distance-distributions separation for unsupervised person re-identification." European Conference on Computer Vision. Springer, Cham, 2020.[Paper]

  • Wu, Si. "An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification." (2020).(ECCV2020)[Paper]

Pure re-ID

Handcraft feature

  • Zheng, Liang, et al. "Scalable person re-identification: A benchmark." Proceedings of the IEEE international conference on computer vision. 2015.[Paper]

  • S. Liao, Y. Hu, X. Zhu, and S. Z. Li, "Person reidentiļ¬cation by local maximal occurrence representation and metric learning." in CVPR, 2015, pp. 21972206.[Paper]

  • Ma, Bingpeng, Yu Su, and FrĆ©dĆ©ric Jurie. "Bicov: a novel image representation for person re-identification and face verification." British Machive Vision Conference. 2012.[Paper]

  • G. Lisanti, I. Masi, A. D. Bagdanov, and A. Del Bimbo, "Person reidentiļ¬cation by iterative re-weighted sparse ranking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 8, pp. 1629ā€“1642, 2015.[Paper]

  • E. Kodirov, T. Xiang, Z. Fu, and S. Gong, "Person re-identiļ¬cation by unsupervised $\ell _1 $ graph learning," in Proc. Eur. Conf. Comput. Vis., 2016, pp. 178ā€“195.[Paper]

  • Yang, Yang, et al. "Unsupervised learning of multi-level descriptors for person re-identification." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 31. No. 1. 2017.[Paper]

  • Kodirov, Elyor, Tao Xiang, and Shaogang Gong. "Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification." BMVC. Vol. 3. 2015.[Paper]

  • Farenzena, Michela, et al. "Person re-identification by symmetry-driven accumulation of local features." 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, 2010.[Paper]

  • Wang, Hanxiao, Shaogang Gong, and Tao Xiang. "Unsupervised learning of generative topic saliency for person re-identification." (2014).[Paper]

  • Zhao, Rui, Wanli Oyang, and Xiaogang Wang. "Person re-identification by saliency learning." IEEE transactions on pattern analysis and machine intelligence 39.2 (2016): 356-370.[Paper]

Clustering-based

  • Wang, Dongkai, and Shiliang Zhang. "Unsupervised person re-identification via multi-label classification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.[Paper]

  • Lin, Yutian, et al. "Unsupervised person re-identification via softened similarity learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.[Paper][Code]

  • Lin, Yutian, et al. "A bottom-up clustering approach to unsupervised person re-identification." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019.[Paper]

  • Ding, Guodong, et al. "Dispersion based Clustering for Unsupervised Person Re-identification." BMVC. 2019.[Paper]

  • Ye, Mang, et al. "Dynamic label graph matching for unsupervised video re-identification." Proceedings of the IEEE international conference on computer vision. 2017.[Paper]

  • Zeng, Kaiwei, et al. "Hierarchical clustering with hard-batch triplet loss for person re-identification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.[Paper]

  • Xuan, Shiyu, and Shiliang Zhang. "Intra-Inter Camera Similarity for Unsupervised Person Re-Identification." arXiv preprint arXiv:2103.11658 (2021).[Paper]

  • Wang, Menglin, et al. "Camera-aware Proxies for Unsupervised Person Re-Identification." arXiv preprint arXiv:2012.10674 (2020).[Paper]

  • Yang, Fengxiang, et al. "Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification." arXiv preprint arXiv:2103.04618 (2021).[Paper]

Tracklet based

  • Wu, Guile, Xiatian Zhu, and Shaogang Gong. "Tracklet self-supervised learning for unsupervised person re-identification." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 07. 2020.[Paper]

  • Li, Minxian, Xiatian Zhu, and Shaogang Gong. "Unsupervised tracklet person re-identification." IEEE transactions on pattern analysis and machine intelligence 42.7 (2019): 1770-1782.[Paper]

  • Li, Minxian, Xiatian Zhu, and Shaogang Gong. "Unsupervised person re-identification by deep learning tracklet association." Proceedings of the European conference on computer vision (ECCV). 2018.[Paper]

  • Ye, Mang, Xiangyuan Lan, and Pong C. Yuen. "Robust anchor embedding for unsupervised video person re-identification in the wild." Proceedings of the European Conference on Computer Vision (ECCV). 2018.[Paper]

  • Ma, Xiaolong, et al. "Person re-identification by unsupervised video matching." Pattern Recognition 65 (2017): 197-210.[Paper]

  • Liu, Zimo, Dong Wang, and Huchuan Lu. "Stepwise metric promotion for unsupervised video person re-identification." Proceedings of the IEEE international conference on computer vision. 2017.[Paper]

  • Xie, Qiaokang, et al. "Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization." IEEE Transactions on Multimedia

Other Unsupervised Learning research in Computer Vision or re-ID related works

  • Fernando, Basura, et al. "Unsupervised visual domain adaptation using subspace alignment." Proceedings of the IEEE international conference on computer vision. 2013.[Paper]

  • Gong, Boqing, et al. "Geodesic flow kernel for unsupervised domain adaptation." 2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012.[Paper]

  • Gopalan, Raghuraman, Ruonan Li, and Rama Chellappa. "Domain adaptation for object recognition: An unsupervised approach." 2011 international conference on computer vision. IEEE, 2011.[Paper]

  • Qiu, Qiang, Jie Ni, and Rama Chellappa. "Dictionary-based domain adaptation methods for the re-identification of faces." Person Re-Identification. Springer, London, 2014. 269-285.[Paper]

  • 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]

  • Hoffman, Judy, et al. "Cycada: Cycle-consistent adversarial domain adaptation." International conference on machine learning. PMLR, 2018.[Paper][Code]

  • Dong, Xuanyi, et al. "Style aggregated network for facial landmark detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.[Paper][Code]

  • Zhao, Jian, et al. "3D-Aided Deep Pose-Invariant Face Recognition." IJCAI. Vol. 2. No. 3. 2018.[Paper]

  • Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.[Paper][Code]

  • Bousmalis, Konstantinos, et al. "Unsupervised pixel-level domain adaptation with generative adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.[Paper][Code]

  • Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Split-brain autoencoders: Unsupervised learning by cross-channel prediction." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.[Paper][Code]

  • Bojanowski, Piotr, and Armand Joulin. "Unsupervised learning by predicting noise." International Conference on Machine Learning. PMLR, 2017.[Paper][Code]

  • Chang, Jianlong, et al. "Deep adaptive image clustering." Proceedings of the IEEE international conference on computer vision. 2017.[Paper][Code]

  • 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]

  • Sun, Yifan, et al. "Svdnet for pedestrian retrieval." Proceedings of the IEEE International Conference on Computer Vision. 2017.[Paper]

  • Lee, Hsin-Ying, et al. "Unsupervised representation learning by sorting sequences." Proceedings of the IEEE International Conference on Computer Vision. 2017.[Paper][Code]

  • Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "Image style transfer using convolutional neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.[Paper]

  • Bousmalis, Konstantinos, et al. "Domain separation networks." arXiv preprint arXiv:1608.06019 (2016).[Paper]

  • Taigman, Yaniv, Adam Polyak, and Lior Wolf. "Unsupervised cross-domain image generation." arXiv preprint arXiv:1611.02200 (2016).[Paper][Code]

  • Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." European conference on computer vision. Springer, Cham, 2016.[Paper][Code]

  • Sun, Baochen, Jiashi Feng, and Kate Saenko. "Return of frustratingly easy domain adaptation." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 30. No. 1. 2016.[Paper]

  • Sun, Baochen, and Kate Saenko. "Deep coral: Correlation alignment for deep domain adaptation." European conference on computer vision. Springer, Cham, 2016.[Paper][Code]

  • Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).[Paper][Code]

  • Bojanowski, Piotr, et al. "Weakly supervised action labeling in videos under ordering constraints." European Conference on Computer Vision. Springer, Cham, 2014.[Paper]

  • Goodfellow I J, Pouget-Abadie J, Mirza M, et al. "Generative adversarial nets." Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014:2672-2680.

  • Tzeng, Eric, et al. "Deep domain confusion: Maximizing for domain invariance." arXiv preprint arXiv:1412.3474 (2014).[Paper][Code]

  • Fernando, Basura, et al. "Unsupervised visual domain adaptation using subspace alignment." Proceedings of the IEEE international conference on computer vision. 2013.[Paper]

  • Gong, Boqing, et al. "Geodesic flow kernel for unsupervised domain adaptation." 2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012.[Paper]

  • Gong, Boqing, et al. "Geodesic flow kernel for unsupervised domain adaptation." 2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012.[Paper]

  • Ma, Bingpeng, Yu Su, and FrĆ©dĆ©ric Jurie. "Local descriptors encoded by fisher vectors for person re-identification." European conference on computer vision. Springer, Berlin, Heidelberg, 2012.[Paper]

Semi-supervised Learning or Few-shot Learning

  • Li, Jiawei, Andy J. Ma, and Pong C. Yuen. "Semi-supervised region metric learning for person re-identification." International Journal of Computer Vision 126.8 (2018): 855-874.

  • Wu, Yu, et al. "Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.[Paper]

  • Su, Chi, et al. "Deep attributes driven multi-camera person re-identification." European conference on computer vision. Springer, Cham, 2016.[Paper]

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Awesome-Unsupervised-Person-Re-identification

person-reidentificationperson-reidperson-re-identificationunsupervised-learning