Unsupervised Person Re-Identification
51 papers with code • 15 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Person Re-Identification
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Market-1501
DukeMTMC-reID
MSMT17
MARS
DukeMTMC-VideoReID
iLIDS-VID
ClonedPerson
Most implemented papers
Joint Discriminative and Generative Learning for Person Re-identification
layumi/Person_reID_baseline_pytorch
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CVPR 2019
To this end, we propose a joint learning framework that couples re-id learning and data generation end-to-end.
Learning Generalisable Omni-Scale Representations for Person Re-Identification
KaiyangZhou/deep-person-reid
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An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
To tackle the challenges, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term.
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
To solve these problems, we propose a novel self-paced contrastive learning framework with hybrid memory.
Cluster Contrast for Unsupervised Person Re-Identification
Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets.
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
yxgeee/MMT
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ICLR 2020
In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner.
Weakly supervised discriminative feature learning with state information for person identification
KovenYu/state-information
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CVPR 2020
We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
In this context, we propose a mesh-based view generator.
Rethinking Sampling Strategies for Unsupervised Person Re-identification
While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role.
Unsupervised Person Re-identification with Stochastic Training Strategy
lithium770/unsupervised-person-re-id-with-stochastic-training-strategy
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State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning.
