Unsupervised-Person-ReID-Paper

Current Unsupervised Person ReID Paper&Code

title abbreviation year d->m(M) m->d(D) code
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification PTGAN CVPR2018 - - https://github.com/pkuvmc/PTGAN
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification TJ-AIDL CVPR2018 23.0/44.3 26.5/58.2 -
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification SPGAN CVPR2018 26.9/58.1 26.4/46.9 https://github.com/Simon4Yan/Learning-via-Translation
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification ARN CVPR2018W 39.4/70.3 33.4/60.2 https://github.com/yujheli/ARN
Generalizing A Person Retrieval Model Hetero- and Homogeneously HHL ECCV2018 31.4/62.2 27.2/46.9 https://github.com/zhunzhong07/HHL
Unsupervised Person Re-identification by Deep Learning Tracklet Association TAUDL ECCV2018 41.2/63.7 43.5/61.7 -
Unsupervised Tracklet Person Re-Identification UTAL TPAMI2019 46.2/69.2 44.6/62.3 -
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification ECN CVPR2019 43.0/75.1 40.4/63.3 https://github.com/zhunzhong07/ECN
Unsupervised Person Re-identification by Soft Multilabel Learning MAR CVPR2019 40.0/67.7* 48.0/67.1* https://github.com/KovenYu/MAR
Unsupervised Person Re-Identification with Iterative Self-Supervised Domain Adaptation ISSDA-ReID CVPR2019W 63.1/81.3 54.1/72.8 -
Patch-based discriminative feature learning for unsupervised person re-identification PAUL CVPR2019 40.1/68.5* 53.2/72.0* https://github.com/QizeYang/PAUL
Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding DECAMEL TPAMI2019 32.44/60.24 - https://github.com/KovenYu/DECAMEL
Learning to Adapt Invariance in Memory for Person Re-identification ECN+GPP TPAMI2019 63.8/84.1 54.4/74.0 -
Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification ACAN TIP? 50.6/73.3 46.6/65.1 -
Unsupervised Person Re-identification: Clustering and Fine-tuning PUL TOMCCAP17 (D,C->M) 24.8/50.9 (M,C->D) 21.5/36.5 https://github.com/hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning
A Bottom-up Clustering Approach to Unsupervised Person Re-identification BUC AAAI2019 (M) 38.3/66.2 29.6/61.9 (D) 27.5/47.4 22.1/40.4 https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification
Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification DBC Arxiv19(TMM?) (M) 41.3/69.2 (D) 30.0/51.5 https://github.com/gddingcs/Dispersion-based-Clustering
Adaptive Exploration for Unsupervised Person Re-Identification AE Arxiv19(TMM?) 54.0/77.5 39.6/63.2 https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification
Hierarchical Clustering-guided re-ID with Triplet loss HRC Arxiv19 (M) 55.3/79.5 (D) 46.8/66.0 -
Consistent Cross-view Matching for Unsupervised Person Re-identification CCM Arxiv19 (M)43.1/71.7 - -
Unsupervised Person Re-identification via Multi-label Classification MMCL CVPR20 45.5/80.3 40.2/65.2 https://github.com/kennethwdk/MLCReID
Unsupervised Person Re-identification via Softened Similarity Learning SSL CVPR20 37.8/71.7 28.6/52.5 -
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* Means trained with MSMT17 dataset, (M) means trained with Market Only, (D) means trained with Duke Only