Unsupervised Person Re-Identification
58 papers with code • 19 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Person Re-Identification
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Datasets
Market-1501
DukeMTMC-reID
MSMT17
MARS
DukeMTMC-VideoReID
iLIDS-VID
Latest papers with no code
Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification
Clustering-based methods, which alternate between the generation of pseudo labels and the optimization of the feature extraction network, play a dominant role in both unsupervised learning (USL) and unsupervised domain adaptive (UDA) person re-identification (Re-ID).
Learning to Purification for Unsupervised Person Re-identification
To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme.
Unsupervised Long-Term Person Re-Identification with Clothes Change
We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment.
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification
The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network.
Multi-Level Attention for Unsupervised Person Re-Identification
The attention mechanism is widely used in deep learning because of its excellent performance in neural networks without introducing additional information.
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification
Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning.
Mind Your Clever Neighbours: Unsupervised Person Re-identification via Adaptive Clustering Relationship Modeling
To generate high-quality pseudo-labels and mitigate the impact of clustering errors, we propose a novel clustering relationship modeling framework for unsupervised person Re-ID.
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms.
Context-Aware Unsupervised Clustering for Person Search
The existing person search methods use the annotated labels of person identities to train deep networks in a supervised manner that requires a huge amount of time and effort for human labeling.
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id
However, since person Re-Id is an open-set problem, the clustering based methods often leave out lots of outlier instances or group the instances into the wrong clusters, thus they can not make full use of the training samples as a whole.
