Unsupervised Person Re-Identification
40 papers with code • 14 benchmarks • 7 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
Most implemented papers
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.
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.
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.
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Inspired by that, a simple yet effective approach is proposed, known as group sampling, which gathers groups of samples from the same class into a mini-batch.
Unsupervised Person Re-identification: Clustering and Fine-tuning
hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning
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Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence.
Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification
While metric learning is important for Person re-identification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training.
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
ahangchen/TFusion
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CVPR 2018
Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.
