Unsupervised Person Re-Identification

40 papers with code • 14 benchmarks • 7 datasets

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Most implemented papers

Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID

yxgeee/VisDA-ECCV20 14 Mar 2020

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

yxgeee/SpCL NeurIPS 2020

To solve these problems, we propose a novel self-paced contrastive learning framework with hybrid memory.

Cluster Contrast for Unsupervised Person Re-Identification

alibaba/cluster-contrast 22 Mar 2021

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 15 Oct 2019

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 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 CVPR 2020

We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.

Rethinking Sampling Strategies for Unsupervised Person Re-identification

ucas-vg/groupsampling 7 Jul 2021

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 30 May 2017

Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence.

Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification

KovenYu/CAMEL ICCV 2017

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 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.