# Awesome Person Re-Identification If you have any problems, suggestions or improvements, please submit the issue or PR. **[2021.12.15] Due to my new research interest -- Gigapixel-Level Analysis, I do not have enough time to maintain this repository. I recommend an active repository created by [Seokeon](https://github.com/bismex/Awesome-person-re-identification).** ## Contents * [Talks](#talks) * [Datasets](#datasets) * [Papers](#papers) * [Leaderboard](#leaderboard) ### Talks * [2020年 行人重识别的挑战 与 最新进展 （35页PPT整理）](https://zhuanlan.zhihu.com/p/163255539) [[video]](https://www.bilibili.com/video/BV11K4y1f7eQ) ### Code - [Pedestrian Alignment Network](https://github.com/layumi/Pedestrian_Alignment) ![GitHub stars](https://img.shields.io/github/stars/layumi/Pedestrian_Alignment.svg?style=flat&label=Star) - [2stream Person re-ID](https://github.com/layumi/2016_person_re-ID) ![GitHub stars](https://img.shields.io/github/stars/layumi/2016_person_re-ID.svg?style=flat&label=Star) - [Pedestrian GAN](https://github.com/layumi/Person-reID_GAN) ![GitHub stars](https://img.shields.io/github/stars/layumi/Person-reID_GAN.svg?style=flat&label=Star) - [Language Person Search](https://github.com/layumi/Image-Text-Embedding) ![GitHub stars](https://img.shields.io/github/stars/layumi/Image-Text-Embedding.svg?style=flat&label=Star) - [DG-Net](https://github.com/NVlabs/DG-Net) ![GitHub stars](https://img.shields.io/github/stars/NVlabs/DG-Net.svg?style=flat&label=Star) - [3D Person re-ID](https://github.com/layumi/person-reid-3d) ![GitHub stars](https://img.shields.io/github/stars/layumi/person-reid-3d.svg?style=flat&label=Star) - [[L1aoXingyu](https://github.com/JDAI-CV/fast-reid)] FastReID Baseline - [[michuanhaohao](https://github.com/michuanhaohao/reid-strong-baseline)] Bag of Tricks and A Strong Baseline for Deep Person Re-identification - [[layumi](https://github.com/michuanhaohao/reid-strong-baseline)] A tiny, friendly, strong pytorch implement of person re-identification baseline ## Datasets | Dataset | Release time | # identities | # cameras | # images | |---------------------------|------------------|--------------|-------------|----------| | [VIPeR](https://vision.soe.ucsc.edu/node/178) | 2007 | 632 | 2 | 1264 | | [ETH1,2,3](http://homepages.dcc.ufmg.br/~william/datasets.html)| 2007 | 85, 35, 28 | 1 | 8580 | | [QMUL iLIDS](http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz) | 2009 | 119 | 2 | 476 | | [GRID](http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html) | 2009 | 1025 | 8 | 1275 | | [CAVIAR4ReID](http://www.lorisbazzani.info/caviar4reid.html) | 2011 | 72 | 2 | 1220 | | [3DPeS](http://www.openvisor.org/3dpes.asp) | 2011 | 192 | 8 | 1011 | | [PRID2011](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/) | 2011 | 934 | 2 | 24541 | | [V47](https://docs.google.com/leaf?id=0B692grTpU3UNZWZlN2I2NWYtYzdhNi00MWJkLWI0YjYtNTg2Zjk1OGFkMGQ1) | 2011 | 47 | 2 | 752 | | [WARD](https://github.com/iN1k1/CVPR2012/tree/master/toolbox/Datasets) | 2012 | 70 | 3 | 4786 | | [SAIVT-Softbio](https://researchdatafinder.qut.edu.au/display/n27416) | 2012 | 152 | 8 | 64472 | | | [CUHK01](http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html) | 2012 | 971 | 2 | 3884 | | [CUHK02](http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html) | 2013 | 1816 | 10(5 pairs) | 7264 | | [CUHK03](http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html) | 2014 | 1467 | 10(5 pairs) | 13164 | | [RAiD](http://cs-people.bu.edu/dasabir/raid.php) | 2014 | 43 | 4 | 6920 | | [iLIDS-VID](http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html) | 2014 | 300 | 2 | 42495 | | [MPR Drone](http://www.eecs.qmul.ac.uk/~rlayne/downloads_qmul_drone_dataset.html) | 2014 | 84 | 1 | | | [HDA Person Dataset](http://vislab.isr.ist.utl.pt/hda-dataset/) | 2014 | 53 | 13 | 2976 | | | [Shinpuhkan Dataset](http://www.mm.media.kyoto-u.ac.jp/en/datasets/shinpuhkan/) | 2014 | 24 | 16 | | | | [CASIA Gait Database B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) | 2015 | 124 | 11 | | | [Market1501](http://www.liangzheng.org/Project/project_reid.html) | 2015 | 1501 | 6 | 32217 | | [PKU-Reid](https://github.com/charliememory/PKU-Reid-Dataset) | 2016 | 114 | 2 | 1824 | | [PRW](http://www.liangzheng.com.cn/Project/project_prw.html) | 2016 | 932 | 6 | 34,304 | | [CUHK-SYSU](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html) | 2016 | 8,432 | - | 18,184 | | | [MARS](http://www.liangzheng.com.cn/Project/project_mars.html) | 2016 | 1261 | 6 | 1,191,003 | | [DukeMTMC-reID](https://github.com/layumi/DukeMTMC-reID_baseline) (offline) | 2017 | 1812 | 8 | 36441 | | | [Airport](http://www.northeastern.edu/alert/transitioning-technology/alert-datasets/alert-airport-re-identification-dataset/) | 2017 | 9651 | 6 | 39902 | | [MSMT17](http://www.pkuvmc.com/dataset.html) | 2018 | 4101 | 15 | 126441 | | [RPIfield](https://drive.google.com/file/d/1GO1zm7vCAJwXgJtoFyUs367_Knz8Ev0A/view?usp=sharing) | 2018 | 112 | 12 | 601,581 | | [LS-VID](http://www.pkuvmc.com/dataset.html) | 2019 | 3,772 | 15 | 2,982,685 | | [PersonX](https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint) | 2019 | 1,266 | 6 | 273,456 | | [COCAS](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.pdf) | 2020 | 5,266 | - | 62,382 | ## Papers ### Survey - Person search: New paradigm of person re-identification: A survey and outlook of recent works (**IVC2020**) [[paper](https://www.sciencedirect.com/science/article/pii/S0262885620301025)] - Deep Learning for Person Re-identification: A Survey and Outlook (**arXiv**) [[arxiv](https://arxiv.org/abs/2001.04193)] - A Survey of Open-World Person Re-identification (**T-CSVT2019**) [[paper](https://ieeexplore.ieee.org/abstract/document/8640834)] - A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets **(T-PAMI2018)** [[paper](https://ieeexplore.ieee.org/document/8294254/)][[github](https://github.com/RSL-NEU/person-reid-benchmark)] [[arxiv](https://arxiv.org/abs/1605.09653)] - Person Re-identification: Past, Present and Future (**arXiv2016**) [[arxiv](https://arxiv.org/abs/1610.02984)] - A survey of approaches and trends in person re-identification (**Image and Vision Computing 2014**) [[paper](https://www.sciencedirect.com/science/article/pii/S0262885614000262)] - Appearance Descriptors for Person Re-identification: a Comprehensive Review (**arXiv2013**) [[arxiv](https://arxiv.org/abs/1307.574)] - People reidentification in surveillance and forensics: A survey (**CSUR2013**) [[paper](https://dl.acm.org/citation.cfm?id=2543596)] - Intelligent multi-camera video surveillance: A review (**PR Letters2013**) [[paper](https://www.sciencedirect.com/science/article/pii/S016786551200219X)] ### Methods dealing with the lack of labelled data - Unsupervised Person Re-Identification via Softened Similarity Learning **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.pdf)] - Unsupervised Person Re-identification via Multi-label Classification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Unsupervised_Person_Re-Identification_via_Multi-Label_Classification_CVPR_2020_paper.pdf)] - Asymetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification **(AAAI2020)** [[arxiv](https://arxiv.org/abs/1912.01349)] - Domain Adaptive Attention Learning for Unsupervised Person Re-Identification **(AAAI2020)** - Tracklet Self-Supervised Learning for Unsupervised Person Re-Identification **(AAAI2020)** [[paper](http://www.eecs.qmul.ac.uk/~sgg/papers/WuEtAl_AAAI2020.pdf)] - **[SHRED]** Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis **(WACV2020)** [[paper](http://openaccess.thecvf.com/content_WACV_2020/html/Kumar_Unsupervised_Domain_Adaptation_in_Person_re-ID_via_k-Reciprocal_Clustering_and_WACV_2020_paper.html)] - **[CamStyle]** CamStyle: A Novel Data Augmentation Method for Person Re-Identification (**TIP2019**) [[paper](https://ieeexplore.ieee.org/document/8485427/)][[github](https://github.com/zhunzhong07/CamStyle)] - **[DGM+]** Dynamic Graph Co-Matching for Unsupervised Video-Based Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8611378)] - **[t-MTL]** Tensor Multi-task Learning for Person Re-identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8889995)] - **[UTAL]** Unsupervised Tracklet Person Re-Identification (**T-PAMI2019**) [[paper](https://ieeexplore.ieee.org/abstract/document/8658110)][[github](https://github.com/liminxian/DukeMTMC-SI-Tracklet)] - **[MAR]** Unsupervised Person Re-identification by Soft Multilabel Learning (**CVPR2019**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_Unsupervised_Person_Re-Identification_by_Soft_Multilabel_Learning_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1903.06325.pdf)] [[github](https://github.com/KovenYu/MAR)] - **[E2E]** Unsupervised Person Image Generation with Semantic Parsing Transformation (**CVPR2019**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Song_Unsupervised_Person_Image_Generation_With_Semantic_Parsing_Transformation_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.03379.pdf)] [[github](https://github.com/SijieSong/person_generation_spt)] - **[PAUL]** Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Patch-Based_Discriminative_Feature_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2019_paper.html)] - **[BUC]** A Bottom-Up Clustering Approach to Unsupervised Person Re-identification (**AAAI2019**) (**Oral**) [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/4898)] [[github](https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification)] - Weakly Supervised Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Meng_Weakly_Supervised_Person_Re-Identification_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.03832.pdf)] - Distilled Person Re-identification: Towards a More Scalable System (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Wu_Distilled_Person_Re-Identification_Towards_a_More_Scalable_System_CVPR_2019_paper.html)] - **[SSG++]** Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification (**ICCV2019**) (**Oral**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.html)][[arxiv](https://arxiv.org/abs/1811.10144)] - **[UCDA-CCE]** A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Qi_A_Novel_Unsupervised_Camera-Aware_Domain_Adaptation_Framework_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1904.03425)] - Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Self-Training_With_Progressive_Augmentation_for_Unsupervised_Cross-Domain_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1907.13315)] - **[UGA]** Unsupervised Graph Association for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Wu_Unsupervised_Graph_Association_for_Person_Re-Identification_ICCV_2019_paper.html)] - **[CFCL]** Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Wu_Unsupervised_Person_Re-Identification_by_Camera-Aware_Similarity_Consistency_Learning_ICCV_2019_paper.html)] - **[PDA-Net]** Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Cross-Dataset_Person_Re-Identification_via_Unsupervised_Pose_Disentanglement_and_Adaptation_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1909.09675)] - Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification (**ICCV2019**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Deep_Reinforcement_Active_Learning_for_Human-in-the-Loop_Person_Re-Identification_ICCV_2019_paper.html)] - **[TJ-AIDL]** Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Transferable_Joint_Attribute-Identity_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1803.09786)] - Unsupervised Cross-Dataset Person Re-Identification by Transfer Learning of Spatial-Temporal Patterns (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Lv_Unsupervised_Cross-Dataset_Person_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1803.07293)] - **[DAsy]** Domain Adaptation through Synthesis for Unsupervised Person Re-identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Slawomir_Bak_Domain_Adaptation_through_ECCV_2018_paper.html)] - **[RACE]** Robust Anchor Embedding for Unsupervised Video Person Re-Identification in the Wild (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Mang_YE_Robust_Anchor_Embedding_ECCV_2018_paper.html)] - **[TAUDL]** Unsupervised Person Re-identification by Deep Learning Tracklet Association (**ECCV2018**)(Oral) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Minxian_Li_Unsupervised_Person_Re-identification_ECCV_2018_paper.html)] - **[CAMEL]** Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Yu_Cross-View_Asymmetric_Metric_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1708.08062)] - Stepwise Metric Promotion for Unsupervised Video Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Stepwise_Metric_Promotion_ICCV_2017_paper.html)] - **[UDAP]** Unsupervised Domain Adaptive Re-Identification: Theory and Practice (**arXiv2018**) [[paper](https://arxiv.org/abs/1807.11334)] - **[ECN]** Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification (**CVPR2019**) [[paper](https://arxiv.org/abs/1904.01990)] - **[CDs]** Clustering and Dynamic Sampling Based Unsupervised Domain Adaptation for Person Re-Identification (**ICME2019**) [[paper](http://www.cbsr.ia.ac.cn/users/zlei/papers/JLWU-ICME-2019.pdf)] - **[ARN]** Adaptation and reidentification network: An unsupervised deep transfer learning approach to person re-identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w6/Li_Adaptation_and_Re-Identification_CVPR_2018_paper.pdf)] - **[HHL]** Generalizing a person retrieval model hetero-and homogeneously (**ECCV2018**) [[paper](https://uploads-ssl.webflow.com/5cd23e823ab9b1f01f815a54/5d10d8990022bdb066a9491d_Generalizing%20A%20Person%20Retrieval%20Model%20Hetero%20and%20Homogeneously.pdf)] - **[DECAMEL]** Unsupervised person re- identification by deep asymmetric metric embedding (**PAMI2019**) [[paper](https://arxiv.org/pdf/1901.10177.pdf)] - **[SPGAN+LMP]** Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Deng_Image-Image_Domain_Adaptation_CVPR_2018_paper.pdf)] - **[MMFA]** Multi-task mid-level feature alignment network for un- supervised cross-dataset person re-identification (**BMVC2018**) [[paper](http://www.bmva.org/bmvc/2018/contents/papers/0244.pdf)] - **[CycleGAN]** Unpaired image-to- image translation using cycle-consistent adversarial networks (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.pdf)] - **[PUL]** Unsupervised person re-identification: Clustering and fine-tuning (**TOMM2018**) [[paper](https://arxiv.org/pdf/1705.10444.pdf)] - **[PTGAN]** Person transfer gan to bridge domain gap for person re-identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Person_Transfer_GAN_CVPR_2018_paper.pdf)] - **[proposed]** Video-Based Person Re-Identification Using Unsupervised Tracklet Matching (**Access2019**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8639924)] - **[DAL]** Deep association learning for unsupervised video person re-identification (**BMVC2018**) [[paper](https://arxiv.org/pdf/1808.07301.pdf)] - **[SMP*]** Stepwise metric promotion for unsupervised video person re-identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Stepwise_Metric_Promotion_ICCV_2017_paper.pdf)] - **[PAM+LOMO]** Multi-shot person re-identification using part appearance mixture (**WACV**) [[paper](http://www-sop.inria.fr/members/Francois.Bremond/Postscript/SalwaSSD18.pdf)] - **[DGM+IDE]** Dynamic label graph matching for unsupervised video re-identification (**ICCV2017**) [[paper](https://arxiv.org/abs/1709.09297v1)] - **[MDTS]** Person re-identification by unsupervised video matching (**PR2017**) [[paper](https://arxiv.org/abs/1611.08512)] ### 2020 - COCAS: A Large-Scale Clothes Changing Person Dataset for Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.pdf)] - Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Online_Joint_Multi-Metric_Adaptation_From_Frequent_Sharing-Subset_Mining_for_Person_CVPR_2020_paper.pdf)] - Style Normalization and Restitution for Generalizable Person Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.pdf)] - Unsupervised Person Re-Identification via Softened Similarity Learning **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.pdf)] - Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking **(CVPR2020)(Oral)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Transferable_Controllable_and_Inconspicuous_Adversarial_Attacks_on_Person_Re-identification_With_CVPR_2020_paper.pdf)] - Inter-Task Association Critic for Cross-Resolution Person Re-Identification **(CVPR2020)(Oral)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Cheng_Inter-Task_Association_Critic_for_Cross-Resolution_Person_Re-Identification_CVPR_2020_paper.pdf)] - Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yan_Learning_Multi-Granular_Hypergraphs_for_Video-Based_Person_Re-Identification_CVPR_2020_paper.pdf)] - Relation-Aware Global Attention for Person Re-identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Relation-Aware_Global_Attention_for_Person_Re-Identification_CVPR_2020_paper.pdf)] - Spatial-Temporal Graph Convolutional Network for Video-based Person Re-identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Spatial-Temporal_Graph_Convolutional_Network_for_Video-Based_Person_Re-Identification_CVPR_2020_paper.pdf)] - Salience-Guided Cascaded Suppression Network for Person Re-identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Salience-Guided_Cascaded_Suppression_Network_for_Person_Re-Identification_CVPR_2020_paper.pdf)] - Bi-directional Interaction Network for Person Search **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.pdf)] - Instance Guided Proposal Network for Person Search **(CVPR2020)(Oral)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Instance_Guided_Proposal_Network_for_Person_Search_CVPR_2020_paper.pdf)] - AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhai_AD-Cluster_Augmented_Discriminative_Clustering_for_Domain_Adaptive_Person_Re-Identification_CVPR_2020_paper.pdf)] - Unity Style Transfer for Person Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Unity_Style_Transfer_for_Person_Re-Identification_CVPR_2020_paper.pdf)] - High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_High-Order_Information_Matters_Learning_Relation_and_Topology_for_Occluded_Person_CVPR_2020_paper.pdf)] - Robust Partial Matching for Person Search in the Wild **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhong_Robust_Partial_Matching_for_Person_Search_in_the_Wild_CVPR_2020_paper.pdf)] - Weakly Supervised Discriminative Feature Learning with State Information for Person Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_Weakly_Supervised_Discriminative_Feature_Learning_With_State_Information_for_Person_CVPR_2020_paper.pdf)] - Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Choi_Hi-CMD_Hierarchical_Cross-Modality_Disentanglement_for_Visible-Infrared_Person_Re-Identification_CVPR_2020_paper.pdf)] - Learning Longterm Representations for Person Re-Identification Using Radio Signals **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Learning_Longterm_Representations_for_Person_Re-Identification_Using_Radio_Signals_CVPR_2020_paper.pdf)] - Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Ahmed_Camera_On-Boarding_for_Person_Re-Identification_Using_Hypothesis_Transfer_Learning_CVPR_2020_paper.pdf)] - Cross-modality Person re-identification with Shared-Specific Feature Transfer **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lu_Cross-Modality_Person_Re-Identification_With_Shared-Specific_Feature_Transfer_CVPR_2020_paper.pdf)] - Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.pdf)] - Real-world Person Re-Identification via Degradation Invariance Learning **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Real-World_Person_Re-Identification_via_Degradation_Invariance_Learning_CVPR_2020_paper.pdf)] - Unsupervised Person Re-identification via Multi-label Classification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Unsupervised_Person_Re-Identification_via_Multi-Label_Classification_CVPR_2020_paper.pdf)] - Smoothing Adversarial Domain Attack and p-Memory Reconsolidation for Cross-Domain Person Re-Identification **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Smoothing_Adversarial_Domain_Attack_and_P-Memory_Reconsolidation_for_Cross-Domain_Person_CVPR_2020_paper.pdf)] - Norm-Aware Embedding for Efficient Person Search **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Norm-Aware_Embedding_for_Efficient_Person_Search_CVPR_2020_paper.pdf)] - TCTS: A Task-Consistent Two-stage Framework for Person Search **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_TCTS_A_Task-Consistent_Two-Stage_Framework_for_Person_Search_CVPR_2020_paper.pdf)] - Pose-guided Visible Part Matching for Occluded Person ReID **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Gao_Pose-Guided_Visible_Part_Matching_for_Occluded_Person_ReID_CVPR_2020_paper.pdf)] - Cross-Modal Cross-Domain Moment Alignment Network for Person Search **(CVPR2020)** [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jing_Cross-Modal_Cross-Domain_Moment_Alignment_Network_for_Person_Search_CVPR_2020_paper.pdf)] - Uncertainty-aware Multi-shot Knowledge Distillation for Image-based Object Re-identification **(AAAI2020)** [[arxiv](https://arxiv.org/abs/2001.05197)] - Infrared-Visible Cross-Modal Person Re-Identification with an X Modality **(AAAI2020)** - Single Camera Training for Person Re-identification **(AAAI2020)** [[arxiv](https://arxiv.org/abs/1909.10848)] - Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification **(AAAI2020)** [[arxiv](https://arxiv.org/abs/2002.04114)] - Relation-Guided Spatial Attention and Temporal Refinement for Video-based Person Re-Identification **(AAAI2020)** - Semantics-Aligned Representation Learning for Person Re-identification **(AAAI2020)** [[arxiv](https://arxiv.org/abs/1905.13143)] - Relation Network for Person Re-identification **(AAAI2020)** [[arxiv](https://arxiv.org/abs/1911.09318)] - Appearance and Motion Enhancement for Video-based Person Re-identification **(AAAI2020)** - Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect **(AAAI2020)** [[arxiv](https://arxiv.org/abs/1911.12512)] - Frame-Guided Region-Aligned Representation for Video Person Re-identification **(AAAI2020)** - Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification **(AAAI2020)** - Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification **(WACV2020)** [[paper](http://openaccess.thecvf.com/content_WACV_2020/html/Khatun_Semantic_Consistency_and_Identity_Mapping_Multi-Component_Generative_Adversarial_Network_for_WACV_2020_paper.html)] - **[SCR]** Learning Discriminative and Generalizable Representations by Spatial-Channel Partition for Person Re-Identification **(WACV2020)** [[paper](http://openaccess.thecvf.com/content_WACV_2020/html/Chen_Learning_Discriminative_and_Generalizable_Representations_by_Spatial-Channel_Partition_for_Person_WACV_2020_paper.html)] - Video Person Re-Identification using Learned Clip Similarity Aggregation **(WACV2020)** [[paper](http://openaccess.thecvf.com/content_WACV_2020/html/Matiyali_Video_Person_Re-Identification_using_Learned_Clip_Similarity_Aggregation_WACV_2020_paper.html)] - Pose Guided Gated Fusion for Person Re-identification **(WACV2020)** [[paper](http://openaccess.thecvf.com/content_WACV_2020/html/Bhuiyan_Pose_Guided_Gated_Fusion_for_Person_Re-identification_WACV_2020_paper.html)] - Temporal Aggregation with Clip-level Attention for Video-based Person Re-identification **(WACV2020)** [[paper](http://openaccess.thecvf.com/content_WACV_2020/html/Li_Temporal_Aggregation_with_Clip-level_Attention_for_Video-based_Person_Re-identification_WACV_2020_paper.html)] - Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification **(WACV2020)** [[paper](http://openaccess.thecvf.com/content_WACV_2020/html/Yuan_Calibrated_Domain-Invariant_Learning_for_Highly_Generalizable_Large_Scale_Re-Identification_WACV_2020_paper.html)] ### 2019 - **[REGCT]** Robust and Efficient Graph Correspondence Transfer for Person Re-identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8709994)] - **[DHA]** Learning Sparse and Identity-preserved Hidden Attributes for Person Re-identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8874954)] - **[RAP]** A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8510891)][[github](https://github.com/dangweili/RAP)] - **[SCAN]** SCAN: Self-and-Collaborative Attention Network for Video Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8703416)][[github](https://github.com/ruixuejianfei/SCAN)] - **[FANN]** Discriminative Feature Learning With Foreground Attention for Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8676064)] - Progressive Learning for Person Re-Identification With One Example **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8607049)][[github](https://github.com/Yu-Wu/One-Example-Person-ReID)] - **[PIE]** Pose-Invariant Embedding for Deep Person Re-Identification **(TIP2019)** [[paper](hhttps://ieeexplore.ieee.org/document/8693885)] - **[UVDL]** Uniform and Variational Deep Learning for RGB-D Object Recognition and Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8715446)] - **[CI-CNN]** Context-Interactive CNN for Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8907836)] - **[k-KISSME]** Kernel Distance Metric Learning Using Pairwise Constraints for Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8469088)][[github](https://github.com/bacnguyencong/k-KISSME)] - **[MpRL]** Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8485730)][[github](https://github.com/Huang-3/MpRL-for-person-re-ID)] - Deep Representation Learning With Part Loss for Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8607050)] - **[TRL]** Video Person Re-Identification by Temporal Residual Learning **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8513884)] - **[STAL]** Spatial-Temporal Attention-Aware Learning for Video-Based Person Re-Identification **(TIP2019)** [[paper](https://ieeexplore.ieee.org/document/8675957)] - **[MuDeep]** Leader-based Multi-Scale Attention Deep Architecture for Person Re-identification **(T-PAMI2019)** [[paper](https://ieeexplore.ieee.org/document/8762210)] - Learning Part-based Convolutional Features for Person Re-identification **(T-PAMI2019)** [[paper](https://ieeexplore.ieee.org/document/8826008)] - **[PGR]** Pose-Guided Representation Learning for Person Re-Identification **(T-PAMI2019)** [[paper](https://ieeexplore.ieee.org/document/8764426)] - **[HGD]** Hierarchical Gaussian Descriptors with Application to Person Re-Identification **(T-PAMI2019)** [[paper](https://ieeexplore.ieee.org/document/8705270)] - A Graph-based Approach for Making Consensus-based Decisions in Image Search and Person Re-identification **(T-PAMI2019)** [[paper](https://ieeexplore.ieee.org/document/8852741)] - **[KPM|RW]** Person Re-identification with Deep Kronecker-Product Matching and Group-shuffling Random Walk **(T-PAMI2019)** [[paper](https://ieeexplore.ieee.org/document/8906139)] - **[MHP]** Fine-Grained Multi-human Parsing (**IJCV2019**) [[paper](https://link.springer.com/article/10.1007/s11263-019-01181-5)] - **[TPI]** Tracking Persons-of-Interest via Unsupervised Representation Adaptation (**IJCV2019**) [[paper](https://link.springer.com/article/10.1007/s11263-019-01212-1)] - **[FCDSC]** Multi-target Tracking in Multiple Non-overlapping Cameras Using Fast-Constrained Dominant Sets **(IJCV2019)** [[paper](https://link.springer.com/article/10.1007/s11263-019-01180-6)] - **[DAN]** Learning Discriminative Aggregation Network for Video-Based Face Recognition and Person Re-identification (**IJCV2019**) [[paper](https://link.springer.com/article/10.1007/s11263-018-1135-x)] - Learning Disentangled Representation for Robust Person Re-identification (**NeurIPS2019**) [[paper](https://papers.nips.cc/paper/8771-learning-disentangled-representation-for-robust-person-re-identification)] - **[AlignedReID++]** AlignedReID++: Dynamically matching local information for person re-identification (**PR2019**) [[paper](https://www.sciencedirect.com/science/article/pii/S0031320319302031)] - **[MSP-CNN]** Multi-level Similarity Perception Network for Person Re-identification (**TOMM2019**) [[paper](https://dl.acm.org/citation.cfm?id=3309881)] - Discriminative Representation Learning for Person Re-identification via Multi-loss Training (**JVCIR2019**) [[paper](https://www.sciencedirect.com/science/article/pii/S1047320319301749)] - **[DG-Net]** Joint Discriminative and Generative Learning for Person Re-identification (**CVPR2019**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Zheng_Joint_Discriminative_and_Generative_Learning_for_Person_Re-Identification_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.07223.pdf)] - **[DSA-reID]** Densely Semantically Aligned Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Densely_Semantically_Aligned_Person_Re-Identification_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1812.08967.pdf)] - **[DIMN]** Generalizable Person Re-identification by Domain-Invariant Mapping Network (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Song_Generalizable_Person_Re-Identification_by_Domain-Invariant_Mapping_Network_CVPR_2019_paper.html)] - **[CASN]** Re-Identification with Consistent Attentive Siamese Networks (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Zheng_Re-Identification_With_Consistent_Attentive_Siamese_Networks_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1811.07487.pdf)] - Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhong_Invariance_Matters_Exemplar_Memory_for_Domain_Adaptive_Person_Re-Identification_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.01990.pdf)] [[github](https://github.com/zhunzhong07/ECN)] - Re-ranking via Metric Fusion for Object Retrieval and Person Re-identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Bai_Re-Ranking_via_Metric_Fusion_for_Object_Retrieval_and_Person_Re-Identification_CVPR_2019_paper.html)] - Progressive Pose Attention Transfer for Person Image Generation (**CVPR2019**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Progressive_Pose_Attention_Transfer_for_Person_Image_Generation_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.03349.pdf)] [[github](https://github.com/tengteng95/Pose-Transfer)] - Learning to Reduce Dual-level Discrepancy for Infrared-Visible Person Re-identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Learning_to_Reduce_Dual-Level_Discrepancy_for_Infrared-Visible_Person_Re-Identification_CVPR_2019_paper.html)] - Text Guided Person Image Synthesis (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Zhou_Text_Guided_Person_Image_Synthesis_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.05118.pdf)] - Learning Context Graph for Person Search (**CVPR2019**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Yan_Learning_Context_Graph_for_Person_Search_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.01830.pdf)] [[github](https://github.com/sjtuzq/person_search_gcn)] - **[QEEPS]** Query-guided End-to-End Person Search (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Munjal_Query-Guided_End-To-End_Person_Search_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1905.01203.pdf)] [[github](https://github.com/munjalbharti/Query-guided-End-to-End-Person-Search)] - Multi-person Articulated Tracking with Spatial and Temporal Embeddings (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Jin_Multi-Person_Articulated_Tracking_With_Spatial_and_Temporal_Embeddings_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1903.09214.pdf)] - Dissecting Person Re-identification from the Viewpoint of Viewpoint (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Dissecting_Person_Re-Identification_From_the_Viewpoint_of_Viewpoint_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1812.02162.pdf)] [[github](https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint)] - **[CAMA]** Towards Rich Feature Discovery with Class Activation Maps Augmentation for Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Towards_Rich_Feature_Discovery_With_Class_Activation_Maps_Augmentation_for_CVPR_2019_paper.html)] - **[VRSTC]** VRSTC: Occlusion-Free Video Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Hou_VRSTC_Occlusion-Free_Video_Person_Re-Identification_CVPR_2019_paper.html)] - **[ATNet]** Adaptive Transfer Network for Cross-Domain Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Adaptive_Transfer_Network_for_Cross-Domain_Person_Re-Identification_CVPR_2019_paper.html)] - **[Pyramid]** Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Zheng_Pyramidal_Person_Re-IDentification_via_Multi-Loss_Dynamic_Training_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1810.12193.pdf)] - **[IANet]** Interaction-and-Aggregation Network for Person Re-identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Hou_Interaction-And-Aggregation_Network_for_Person_Re-Identification_CVPR_2019_paper.html)] - Skin-based identification from multispectral image data using CNNs (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Uemori_Skin-Based_Identification_From_Multispectral_Image_Data_Using_CNNs_CVPR_2019_paper.html)] - **[VPM]** Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Perceive_Where_to_Focus_Learning_Visibility-Aware_Part-Level_Features_for_Partial_CVPR_2019_paper.html)] [[github](https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint)] - Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Attribute-Driven_Feature_Disentangling_and_Temporal_Aggregation_for_Video_Person_Re-Identification_CVPR_2019_paper.html)] - DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Ge_DeepFashion2_A_Versatile_Benchmark_for_Detection_Pose_Estimation_Segmentation_and_CVPR_2019_paper.html)] - **[AANe]** AANet: Attribute Attention Network for Person Re-Identifications (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Tay_AANet_Attribute_Attention_Network_for_Person_Re-Identifications_CVPR_2019_paper.html)] - Re-Identification Supervised Texture Generation (**CVPR2019**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Re-Identification_Supervised_Texture_Generation_CVPR_2019_paper.html)] [[arxiv](https://arxiv.org/pdf/1904.03385.pdf)] - **[st-ReID]** Spatial-Temporal Person Re-identification (**AAAI2019**) [[arxiv](https://arxiv.org/abs/1812.03282)] [[github](https://github.com/Wanggcong/Spatial-Temporal-Re-identification)] - Learning Resolution-Invariant Deep Representations for Person Re-Identification (**AAAI2019**)(**Oral**) [[paper](https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4832)] - **[HSME]** HSME: Hypersphere Manifold Embedding for Visible Thermal Person Re-identification (**AAAI2019**) [[paper](https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4853)] - **[HPM]** Horizontal Pyramid Matching for Person Re-identification (**AAAI2019**) [[arxiv](https://arxiv.org/abs/1804.05275)] - **[STA]** STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification (**AAAI2019**) [[arxiv](https://arxiv.org/abs/1811.04129)] - Multi-scale 3D Convolution Network for Video Based Person Re-Identification (**AAAI2019**) [[arxiv](https://arxiv.org/abs/1811.07468)] - Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification (**AAAI2019**) [[arxiv](https://arxiv.org/abs/1901.06140)] [[github](https://github.com/youngminPIL/rollback)] - Spatial and Temporal Mutual Promotion for Video-based Person Re-identification (**AAAI2019**) [[arxiv](https://arxiv.org/abs/1812.10305)] - Learning Incremental Triplet Margin for Person Re-identification (**AAAI2019**) [[arxiv](https://arxiv.org/abs/1812.06576)] - **[KVM-MN]** Learning A Key-Value Memory Co-Attention Matching Network for Person ReIdentification (**AAAI2019**) [[paper](https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4959)] - **[ABD-Net]** ABD-Net: Attentive but Diverse Person Re-Identification (**ICCV2019**) [[github](https://github.com/TAMU-VITA/ABD-Net)] [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_ABD-Net_Attentive_but_Diverse_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1908.01114)] - **[advPattern]** advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_advPattern_Physical-World_Attacks_on_Deep_Person_Re-Identification_via_Adversarially_Transformable_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1908.09327)] - Instance-Guided Context Rendering for Cross-Domain Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Instance-Guided_Context_Rendering_for_Cross-Domain_Person_Re-Identification_ICCV_2019_paper.html)] - Mixed High-Order Attention Network for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Mixed_High-Order_Attention_Network_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1908.05819)] - Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Recover_and_Identify_A_Generative_Dual_Model_for_Cross-Resolution_Person_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1908.06052)] - Pose-Guided Feature Alignment for Occluded Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Miao_Pose-Guided_Feature_Alignment_for_Occluded_Person_Re-Identification_ICCV_2019_paper.html)] - Robust Person Re-Identification by Modelling Feature Uncertainty (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.html)] - Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Subramaniam_Co-Segmentation_Inspired_Attention_Networks_for_Video-Based_Person_Re-Identification_ICCV_2019_paper.html)] - RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_RGB-Infrared_Cross-Modality_Person_Re-Identification_via_Joint_Pixel_and_Feature_Alignment_ICCV_2019_paper.html)] - Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Guo_Beyond_Human_Parts_Dual_Part-Aligned_Representations_for_Person_Re-Identification_ICCV_2019_paper.html)] - Batch DropBlock Network for Person Re-Identification and Beyond (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Dai_Batch_DropBlock_Network_for_Person_Re-Identification_and_Beyond_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1811.07130)] - Omni-Scale Feature Learning for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Omni-Scale_Feature_Learning_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1905.00953)] - Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Quan_Auto-ReID_Searching_for_a_Part-Aware_ConvNet_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1903.09776)] - Second-Order Non-Local Attention Networks for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Xia_Second-Order_Non-Local_Attention_Networks_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1909.00295)] - Global-Local Temporal Representations for Video Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Global-Local_Temporal_Representations_for_Video_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1908.10049)] [[github](https://github.com/kanei1024/GLTR)] - Spectral Feature Transformation for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Luo_Spectral_Feature_Transformation_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1811.11405)] - View Confusion Feature Learning for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_View_Confusion_Feature_Learning_for_Person_Re-Identification_ICCV_2019_paper.html)] - MVP Matching: A Maximum-Value Perfect Matching for Mining Hard Samples, With Application to Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Sun_MVP_Matching_A_Maximum-Value_Perfect_Matching_for_Mining_Hard_Samples_ICCV_2019_paper.html)] - Discriminative Feature Learning With Consistent Attention Regularization for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Discriminative_Feature_Learning_With_Consistent_Attention_Regularization_for_Person_Re-Identification_ICCV_2019_paper.html)] - Foreground-Aware Pyramid Reconstruction for Alignment-Free Occluded Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/He_Foreground-Aware_Pyramid_Reconstruction_for_Alignment-Free_Occluded_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1904.04975)] - SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Huang_SBSGAN_Suppression_of_Inter-Domain_Background_Shift_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1908.09086)] - Self-Critical Attention Learning for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Self-Critical_Attention_Learning_for_Person_Re-Identification_ICCV_2019_paper.html)] - Temporal Knowledge Propagation for Image-to-Video Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Gu_Temporal_Knowledge_Propagation_for_Image-to-Video_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1908.03885)] - Deep Constrained Dominant Sets for Person Re-Identification (**ICCV2019**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Alemu_Deep_Constrained_Dominant_Sets_for_Person_Re-Identification_ICCV_2019_paper.html)] [[arxiv](https://arxiv.org/abs/1904.11397)] ### 2018 - **[MGN]** Learning Discriminative Features with Multiple Granularities for Person Re-Identification (**ACMMM2018**) [[paper](https://dl.acm.org/citation.cfm?id=3240552)] - **[FD-GAN]** FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification (**NeurIPS2018**) [[paper](http://papers.nips.cc/paper/7398-fd-gan-pose-guided-feature-distilling-gan-for-robust-person-re-identification)] - **[Multi-pseudo]** Multi-pseudo regularized label for generated data in person re-identification (**TIP2018**) [[paper](https://ieeexplore.ieee.org/abstract/document/8485730/)] [[arxiv](https://arxiv.org/pdf/1801.06742.pdf)] - **[PAN]** Pedestrian Alignment Network for Large-scale Person Re-identification (**T-CSVT2018**) [[paper](https://ieeexplore.ieee.org/abstract/document/8481710)] [[arxiv](https://arxiv.org/pdf/1707.00408.pdf)] - Person Transfer GAN to Bridge Domain Gap for Person Re-Identification (**CVPR2018**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Wei_Person_Transfer_GAN_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1711.08565)] - Disentangled Person Image Generation (**CVPR2018**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Ma_Disentangled_Person_Image_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1712.02621)] - Group Consistent Similarity Learning via Deep CRF for Person Re-Identification (**CVPR2018**)(**Oral**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Group_Consistent_Similarity_CVPR_2018_paper.html)] - Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Diversity_Regularized_Spatiotemporal_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1803.09882)] - A Pose-Sensitive Embedding for Person Re-Identification With Expanded Cross Neighborhood Re-Ranking (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Sarfraz_A_Pose-Sensitive_Embedding_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1711.10378)] - Image-Image Domain Adaptation With Preserved Self-Similarity and Domain-Dissimilarity for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Deng_Image-Image_Domain_Adaptation_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1711.07027)] - Human Semantic Parsing for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Kalayeh_Human_Semantic_Parsing_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1804.00216)] - Video Person Re-Identification With Competitive Snippet-Similarity Aggregation and Co-Attentive Snippet Embedding (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Video_Person_Re-Identification_CVPR_2018_paper.html)] - Mask-Guided Contrastive Attention Model for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Song_Mask-Guided_Contrastive_Attention_CVPR_2018_paper.html)] - Person Re-Identification With Cascaded Pairwise Convolutions (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Person_Re-Identification_With_CVPR_2018_paper.html)] - **[MLFN]** Multi-Level Factorisation Net for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Chang_Multi-Level_Factorisation_Net_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1803.09132)] - Attention-Aware Compositional Network for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1805.03344)] - Deep Group-Shuffling Random Walk for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Shen_Deep_Group-Shuffling_Random_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1807.11178)] - **[HA-CNN]** Harmonious Attention Network for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Harmonious_Attention_Network_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1802.08122)] - Efficient and Deep Person Re-Identification Using Multi-Level Similarity (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Guo_Efficient_and_Deep_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1803.11353)] - **[PT]** Pose Transferrable Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Pose_Transferrable_Person_CVPR_2018_paper.html)] - Adversarially Occluded Samples for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Huang_Adversarially_Occluded_Samples_CVPR_2018_paper.html)] - Camera Style Adaptation for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Zhong_Camera_Style_Adaptation_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1711.10295)] - Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Exploit_the_Unknown_CVPR_2018_paper.html)] - Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Si_Dual_Attention_Matching_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1803.09937)] - Easy Identification From Better Constraints: Multi-Shot Person Re-Identification From Reference Constraints (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_Easy_Identification_From_CVPR_2018_paper.html)] - Eliminating Background-Bias for Robust Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Tian_Eliminating_Background-Bias_for_CVPR_2018_paper.html)] - End-to-End Deep Kronecker-Product Matching for Person Re-Identification (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1807.11182)] - Exploiting Transitivity for Learning Person Re-Identification Models on a Budget (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Roy_Exploiting_Transitivity_for_CVPR_2018_paper.html)] - Deep Spatial Feature Reconstruction for Partial Person Re-Identification: Alignment-Free Approach (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/He_Deep_Spatial_Feature_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1801.00881)] - Resource Aware Person Re-Identification Across Multiple Resolutions (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Resource_Aware_Person_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1805.08805)] - **[DeformGAN]** Deformable GANs for Pose-Based Human Image Generation (**CVPR2018**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Siarohin_Deformable_GANs_for_CVPR_2018_paper.html)] [[arxiv](https://arxiv.org/abs/1801.00055)] - Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/T_M_Feroz_Ali_Maximum_Margin_Metric_ECCV_2018_paper.html)] - RCAA: Relational Context-Aware Agents for Person Search (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.html)] - Generalizing A Person Retrieval Model Hetero- and Homogeneously (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.html)] - Person Search in Videos with One Portrait Through Visual and Temporal Links (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Qingqiu_Huang_Person_Search_in_ECCV_2018_paper.html)] [[github](https://github.com/hqqasw/person-search-PPCC)] - Person Search by Multi-Scale Matching (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Xu_Lan_Person_Search_by_ECCV_2018_paper.html)] - Person Re-identification with Deep Similarity-Guided Graph Neural Network (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Yantao_Shen_Person_Re-identification_with_ECCV_2018_paper.html)] - **[PN-GAN]** Pose-Normalized Image Generation for Person Re-identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Xuelin_Qian_Pose-Normalized_Image_Generation_ECCV_2018_paper.html)] - Person Search via A Mask-guided Two-stream CNN Model (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Di_Chen_Person_Search_via_ECCV_2018_paper.html)] - Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Dapeng_Chen_Improving_Deep_Visual_ECCV_2018_paper.html)] - Hard-Aware Point-to-Set Deep Metric for Person Re-identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Rui_Yu_Hard-Aware_Point-to-Set_Deep_ECCV_2018_paper.html)] - Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-Identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Nikolaos_Karianakis_Reinforced_Temporal_Attention_ECCV_2018_paper.html)] - Adversarial Open-World Person Re-Identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Xiang_Li_Adversarial_Open-World_Person_ECCV_2018_paper.html)] - **[Part-aligned]** Part-Aligned Bilinear Representations for Person Re-Identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Yumin_Suh_Part-Aligned_Bilinear_Representations_ECCV_2018_paper.html)] - **[Mancs]** Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Cheng_Wang_Mancs_A_Multi-task_ECCV_2018_paper.html)] - **[PCB]** Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) (**ECCV2018**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Yifan_Sun_Beyond_Part_Models_ECCV_2018_paper.html)] ### 2017 - **[SHaPE]** SHaPE: A Novel Graph Theoretic Algorithm for Making Consensus-Based Decisions in Person Re-Identification Systems (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Barman_SHaPE_A_Novel_ICCV_2017_paper.html)] - Spatio-Temporal Person Retrieval via Natural Language Queries (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Yamaguchi_Spatio-Temporal_Person_Retrieval_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1704.07945)] - A Two Stream Siamese Convolutional Neural Network for Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Chung_A_Two_Stream_ICCV_2017_paper.html)] - Efficient Online Local Metric Adaptation via Negative Samples for Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Zhou_Efficient_Online_Local_ICCV_2017_paper.html)] - Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Zheng_Learning_View-Invariant_Features_ICCV_2017_paper.html)] - Deeply-Learned Part-Aligned Representations for Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Zhao_Deeply-Learned_Part-Aligned_Representations_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1707.07256)] - **[LSRO]** Unlabeled Samples Generated by GAN Improve the Person Re-Identification Baseline in Vitro (**ICCV2017**)(**Spotlight**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Zheng_Unlabeled_Samples_Generated_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1701.07717)] - Pose-Driven Deep Convolutional Model for Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Su_Pose-Driven_Deep_Convolutional_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1709.08325)] - Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Xu_Jointly_Attentive_Spatial-Temporal_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1708.02286)] - RGB-Infrared Cross-Modality Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Wu_RGB-Infrared_Cross-Modality_Person_ICCV_2017_paper.html)] - Multi-Scale Deep Learning Architectures for Person Re-Identification (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Qian_Multi-Scale_Deep_Learning_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1709.05165)] - **[SVDNet]** SVDNet for Pedestrian Retrieval (**ICCV2017**) [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Sun_SVDNet_for_Pedestrian_ICCV_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1703.05693)] - **[DCF]** Learning Deep Context-Aware Features Over Body and Latent Parts for Person Re-Identification (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Learning_Deep_Context-Aware_CVPR_2017_paper.html)] - Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-Identification (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Chen_Beyond_Triplet_Loss_CVPR_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1704.01719)] - Spindle Net: Person Re-Identification With Human Body Region Guided Feature Decomposition and Fusion (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Zhao_Spindle_Net_Person_CVPR_2017_paper.html)] - Re-Ranking Person Re-Identification With k-Reciprocal Encoding (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1701.08398)] - Person Re-Identification in the Wild (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Zheng_Person_Re-Identification_in_CVPR_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1604.02531)] - **[SSM]** Scalable Person Re-Identification on Supervised Smoothed Manifold (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Bai_Scalable_Person_Re-Identification_CVPR_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1703.08359)] - One-Shot Metric Learning for Person Re-Identification (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Bak_One-Shot_Metric_Learning_CVPR_2017_paper.html)] - Joint Detection and Identification Feature Learning for Person Search (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Xiao_Joint_Detection_and_CVPR_2017_paper.html)] [[arxiv](https://arxiv.org/abs/1604.01850)] - Multiple People Tracking by Lifted Multicut and Person Re-Identification (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Tang_Multiple_People_Tracking_CVPR_2017_paper.html)] - Point to Set Similarity Based Deep Feature Learning for Person Re-Identification (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Zhou_Point_to_Set_CVPR_2017_paper.html)] - Fast Person Re-Identification via Cross-Camera Semantic Binary Transformation (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Chen_Fast_Person_Re-Identification_CVPR_2017_paper.html)] - See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Zhou_See_the_Forest_CVPR_2017_paper.html)] - Consistent-Aware Deep Learning for Person Re-Identification in a Camera Network (**CVPR2017**) [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Lin_Consistent-Aware_Deep_Learning_CVPR_2017_paper.html)] ## Leaderboard The section is being continually updated. Note that some values have superscript, which indicates their source. ### Market-1501 | Year-Conference/Journal | Method | Rank@1 | mAP | | --- | --- | --- | --- | | 2017--CVPR | [DCF](#DCF) | 80.3 | 57.5 | | 2017--CVPR | [SSM](#SSM) | 82.2 | 68.8 | | 2017--ICCV | [SVDNet](#SVDNet) | 82.3 | 62.1 | | 2017--ICCV| [LSRO](#LSRO) | 84.0 | 66.1 | | 2018--CVPR| [DeformGAN](#DeformGAN) | 80.6 | 61.3 | | 2018--T-CSVT| [PAN](#PAN) | 82.8 | 63.4 | | 2018--TIP| [Multi-pseudo](#Multi-pseudo)| 85.8 | 67.5 | | 2018--CVPR| [PT](#PT) | 87.7 | 68.9 | | 2018--ECCV| [PN-GAN](#PN-GAN) | 89.4 | 72.6 | | 2018--CVPR| [MLFN](#MLFN) | 90.0 | 74.3 | | 2018--NeurIPS| [FD-GAN](#FD-GAN) | 90.5 | 77.7 | | 2018--CVPR| [HA-CNN](#HA-CNN) | 91.2 | 75.7 | | 2018--ECCV| [Part-aligned](#Part-aligned) | 91.7 | 79.6 | | 2018--ECCV| [Mancs](#Mancs) | 93.1 | 82.3 | | 2018--ACMMM| [MGN](#MGN) | 95.7 | 86.9 | | 2019--PR| [AlignedReID++](#AlignedReID++) | 91.0 | 77.6 | | 2019--CVPR| [VPM](#VPM) | 93.0 | 80.8 | | 2019--CVPR| [AANet](#AANet) | 93.93 | 83.41 | | 2019--AAAI| [HPM](#HPM) | 94.2 | 82.7 | | 2019--CVPR| [CASN](#CASN) | 94.4 | 82.8 | | 2019--CVPR| [IANet](#IANet) | 94.4 | 83.1 | | 2019--CVPR| [CAMA](#CAMA) | 94.7 | 84.5 | | 2019--CVPR| [DG-Net](#DG-Net) | 94.8 | 86.0 | | 2019--ICCV| [ABD-Net](#ABD-Net) | 95.60 | **88.28** | | 2019--CVPR| [DSA-reID](#DSA-reID) | 95.7 | 87.6 | | 2019--CVPR| [Pyramid](#Pyramid) | 95.7 | 88.2 | | 2019--AAAI| [st-ReID](#st-ReID) | **97.2** | 86.7 | ### DukeMTMC-reID | Year-Conference/Journal | Method | Rank@1 | mAP | | --- | --- | --- | --- | | 2018--NeurIPS| [FD-GAN](#FD-GAN) | 80.0 | 64.5 | | 2018--ECCV| [Mancs](#Mancs) | 84.9 | 71.8 | | 2018--ACMMM| [MGN](#MGN) | 88.7 | 78.4 | | 2019--PR| [AlignedReID++](#AlignedReID++) | 80.7 | 68.0 | | 2019--CVPR| [VPM](#VPM) | 83.6 | 72.6 | | 2019--CVPR| [CAMA](#CAMA) | 85.8 | 72.9 | | 2019--CVPR| [DSA-reID](#DSA-reID) | 86.2 | 74.3 | | 2019--AAAI| [HPM](#HPM) | 86.6 | 74.3 | | 2019--CVPR| [DG-Net](#DG-Net) | 86.6 | 74.8 | | 2019--CVPR| [IANet](#IANet) | 87.1 | 73.4 | | 2019--CVPR| [AANet](#AANet) | 87.65 | 74.29 | | 2019--CVPR| [CASN](#CASN) | 87.7 | 73.7 | | 2019--ICCV| [ABD-Net](#ABD-Net) | 89.0 | 78.59 | | 2019--CVPR| [Pyramid](#Pyramid) | 89.0 | 79.0 | | 2019--AAAI| [st-ReID](#st-ReID) | **94.0** | **82.8** | ### MSMT17 | Year-Conference/Journal | Method | Rank@1 | mAP | | --- | --- | --- | --- | | 2019--CVPR| [IANet](#IANet) | 75.5 | 46.8 | | 2019--CVPR| [DG-Net](#DG-Net) | **77.2** | **52.3** | ## UDA ### Market-1501 | Year-Conference/Journal | Method | Rank@1 | Rank@5 | Rank@10 | mAP | | --- | --- | --- | --- | --- | --- | | 2019--ICCV| [UGA](#UGA) | 87.2 | | | 70.3 | | 2019--ICCV| [SSG++](#SSG++)| 86.2 | 94.6 | 96.5 | 68.7 | | 2018--arXiv|[UDAP](#UDAP) | 75.8 | 89.5 | 93.2 | 53.7 | | 2019--ICCV| [PDA-Net](#PDA-Net)| 75.2 | 86.3 | 90.2 | 47.6 | | 2019--CVPR| [ECN](#ECN) | 75.1 | 87.6 | 91.6 | 43.0 | | 2019--ICME| [CDs](#CDs) | 71.6 | 81.2 | 84.7 | 39.9 | | 2018--CVPR| [ARN](#ARN) | 70.3 | 80.4 | 86.3 | 39.4 | | 2019--PAMI| [UTAL](#UTAL) | 69.2 | | | 46.2 | | 2019--CVPR| [PAUL](#PAUL) | 68.5 | 82.4 | 87.4 | 40.1 | | 2019--CVPR| [MAR](#MAR) | 67.7 | 81.9 | | 40.0 | | 2018--ECCV| [DASy](#DAsy) | 65.7 | | | | | 2019--ICCV| [CFCL](#CFCL) | 65.4 | 80.6 | 86.2 | 35.5 | | 2019--ICCV| [UCDA-CCE](#UCDA-CCE)| 64.3 || | 34.5 | | 2018--ECCV| [TAUDL](#TAUDL)| 63.7 | | | 41.2 | | 2018--ECCV| [HHL](#HHL) | 62.2 | 78.8 | 84.0 | 31.4 | | 2019--AAAI| [BUC](#BUC) | 61.9 | 73.5 | 78.2 | 29.6 | | 2019--PAMI| [DECAMEL](#DECAMEL)| 60.2 | 76.0 | | 32.4 | | 2019--TIP | [CamStyle](#CamStyle)| 58.8 | 78.2 | 84.3 | 31.4 | | 2018--CVPR| [TJ-AIDL](#TJ-AIDL)| 58.2 | 74.8 | 81.1 | 26.5 | | 2018--CVPR| [SPGAN+LMP](#SPGAN+LMP)| 57.7 | 75.8 | 82.4 | 26.7 | | 2018--BMVC| [MMFA](#MMFA) | 56.7 | 75.0 | 81.8 | 27.4 | | 2017--ICCV| [CAMEL](#CAMEL)| 54.5 | 73.1 | | 26.3 | | 2018--CVPR| [SPGAN](#SPGAN)| 51.5 | 70.1 | 76.8 | 27.1 | ### DukeMTMC-reID | Year-Conference/Journal | Method | Rank@1 | Rank@5 | Rank@10 | mAP | | --- | --- | --- | --- | --- | --- | | 2019--ICCV| [SSG++](#SSG++)| 76.0 | 85.8 | 89.3 | 60.3 | | 2019--ICCV| [UGA](#UGA) | 75.0 | | | 53.3 | | 2019--CVPR| [PAUL](#PAUL) | 72.0 | 82.7 | 86.0 | 53.2 | | 2019--PAMI| [UTAL](#UTAL) | 69.2 | | | 46.2 | | 2018--arXiv|[UDAP](#UDAP) | 68.4 | 80.1 | 83.5 | 49.0 | | 2019--ICME| [CDs](#CDs) | 67.2 | 75.9 | 79.4 | 42.7 | | 2019--CVPR| [MAR](#MAR) | 67.1 | 79.8 | | 48.0 | | 2019--CVPR| [ECN](#ECN) | 63.3 | 75.8 | 80.4 | 40.4 | | 2019--ICCV| [PDA-Net](#PDA-Net)| 63.2 | 77.0 | 82.5 | 45.1 | | 2018--ECCV| [TAUDL](#TAUDL)| 61.7 | | | 43.5 | | 2018--CVPR| [ARN](#ARN) | 60.2 | 73.9 | 79.5 | 33.4 | | 2019--ICCV| [CFCL](#CFCL) | 59.3 | 73.2 | 77.8 | 37.8 | | 2019--ICCV| [UCDA-CCE](#UCDA-CCE)| 55.4 || | 36.7 | | 2019--TIP | [CamStyle](#CamStyle)| 48.4 | 62.5 | 68.9 | 25.1| | 2018--ECCV| [HHL](#HHL) | 46.9 | 61.0 | 66.7 | 27.2 | | 2018--CVPR| [SPGAN+LMP](#SPGAN+LMP)| 46.4 | 62.3 | 68.0 | 26.2 | | 2018--BMVC| [MMFA](#MMFA) | 45.3 | 59.8 | 66.3 | 24.7 | | 2018--CVPR| [TJ-AIDL](#TJ-AIDL)| 44.3 | 59.6 | 65.0 | 23.0 | | 2018--CVPR| [SPGAN](#SPGAN)| 41.1 | 56.6 | 63.0 | 22.3 | | 2019--AAAI| [BUC](#BUC) | 40.4 | 52.5 | 58.2 | 22.1 | | 2017--ICCV| [CAMEL](#CAMEL)| 40.3 | 57.6 | | 19.8 | | 2017--ICCV| [CycleGAN](#CycleGAN)| 38.5 || | 19.9 | | 2018--TOMM| [PUL](#PUL) | 30.0 | 43.4 | 48.5 | 16.4 | | 2018--CVPR| [PTGAN](#PTGAN)| 27.4 | 43.6 | 50.7 | 13.5 | ### PRID2011 | Year-Conference/Journal | Method | Rank@1 | Rank@5 | Rank@10 | Rank@20 | | --- | --- | --- | --- | --- | --- | | 2019--Access| [proposed](#proposed)| 91.7 | 96.7 | | 98.7 | | 2018--BMVC| [DAL](#DAL) | 85.3 | 97.0 | | 99.6 | | 2019--TIP | [DGM+](#DGM+) | 81.4 | 95.8 | 98.3 | 99.6 | | 2017--ICCV| [SMP*](#SMP*) | 80.9 | 93.3 | 97.8 | 99.4 | | 2017--ICCV| [DGM+MLAPG](#DGM+MLAPG)| 73.1 | 92.5 | | 99.0 | | 2017--WACV| [PAM+LOMO](#PAM+LOMO)| 70.6 | 90.2 ||97.1 | | 2017--ICCV| [DGM+IDE](#DGM+IDE)| 56.4 | 81.3 | | 96.4 | | 2019--PAMI| [UTAL](#UTAL) | 54.7 | 83.1 | | 96.2 | | 2018--ECCV| [RACE](#RACE) | 50.6 | 79.4 | | 91.8 | | 2018--ECCV| [TAUDL](#TAUDL)|49.4 | 78.7 | | 98.9 | | 2018--ECCV| [DAsy](#DAsy) | 43.0 |||| | 2017--PR | [MDTS](#MDTS) | 41.7 | 67.1 | 79.4 | 90.1 | ### iLIDS-VID | Year-Conference/Journal | Method | Rank@1 | Rank@5 | Rank@10 | Rank@20 | | --- | --- | --- | --- | --- | --- | | 2019--Access| [proposed](#proposed)| 79.1 | 93.5 || 97.5 | | 2018--BMVC| [DAL](#DAL) | 56.9 | 80.6 || 91.9 | | 2018--ECCV| [DAsy](#DAsy) | 56.5 |||| | 2017--ICCV| [SMP*](#SMP*) | 41.7 | 66.3 | 74.1 | 80.7 | | 2017--ICCV| [DGM+MLAPG](#DGM+MLAPG)| 37.1 | 61.3 || 82.0 | | 2019--PAMI| [UTAL](#UTAL) | 35.1 | 59.0 | | 83.8 | | 2017--WACV| [PAM+LOMO](#PAM+LOMO)| 33.3 | 57.8 ||80.5 | | 2017--PR | [MDTS](#MDTS) | 31.5 | 62.1 | 72.8 | 82.4 | | 2018--ECCV| [TAUDL](#TAUDL)|26.7 | 51.3 | | 82.0 | | 2018--ECCV| [RACE](#RACE) | 19.3 | 39.3 | | 68.7 | ### MARS | Year-Conference/Journal | Method | Rank@1 | Rank@5 | Rank@20 | mAP | | --- | --- | --- | --- | --- | --- | | 2019--PAMI| [UTAL](#UTAL) | 49.9 | 66.4 | 77.8 | 35.2 | | 2018--BMVC| [DAL](#DAL) | 46.8 | 63.9 | 77.5 | 21.4 | | 2018--ECCV| [TAUDL](#TAUDL)|43.8 | 59.9 | 72.8 | 29.1 | | 2018--ECCV| [RACE](#RACE) | 43.2 | 57.1 | 67.6 | 24.5 | | 2019--Access| [proposed](#proposed)| 39.7 | 53.2 | 64.1 | 20.1 | | 2017--ICCV| [DGM+MLAPG](#DGM+MLAPG)| 24.6 | 42.6 | 57.2 | 11.8 | | 2017--ICCV| [SMP*](#SMP*) | 23.9 | 35.8 | 44.9 | 10.5 |