Unsupervised Person Re-Identification Edit Task
Computer Vision • Person Re-Identification
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Benchmarks
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Datasets
Greatest papers with code
Learning Generalisable Omni-Scale Representations for Person Re-Identification
• KaiyangZhou/deep-person-reid
• 
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.
UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED PERSON RE-IDENTIFICATION
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
ICLR 2020
• yxgeee/MMT
•
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.
UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED PERSON RE-IDENTIFICATION
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
CVPR 2018
• ahangchen/TFusion
• 
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.
INCREMENTAL LEARNING LEARNING-TO-RANK TRANSFER LEARNING UNSUPERVISED PERSON RE-IDENTIFICATION
Unsupervised Person Re-identification by Soft Multilabel Learning
CVPR 2019
• KovenYu/MAR
•
To overcome this problem, we propose a deep model for the soft multilabel learning for unsupervised RE-ID.
Ranked #60 on
Person Re-Identification
on DukeMTMC-reID
Unsupervised Person Re-identification: Clustering and Fine-tuning
• hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning
•
Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence.
Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification
Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i. e. the number of independent identities from the target domain is unknown).
ONE-SHOT LEARNING UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED PERSON RE-IDENTIFICATION
Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification
Specifically, we develop a PatchNet to select patches from the feature map and learn discriminative features for these patches.
Deep Association Learning for Unsupervised Video Person Re-identification
• yanbeic/Deep-Association-Learning
•
In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training.
Ranked #4 on
Person Re-Identification
on PRID2011
UNSUPERVISED PERSON RE-IDENTIFICATION UNSUPERVISED REPRESENTATION LEARNING VIDEO-BASED PERSON RE-IDENTIFICATION
Weakly supervised discriminative feature learning with state information for person identification
CVPR 2020
• KovenYu/state-information
•
We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.
FACE RECOGNITION PERSON IDENTIFICATION RECTIFICATION ROBUST FACE RECOGNITION UNSUPERVISED PERSON RE-IDENTIFICATION
Unsupervised Person Re-identification via Softened Similarity Learning
CVPR 2020
• ryanaleksander/softened-similarity-learning
•
Person re-identification (re-ID) is an important topic in computer vision.
CLASSIFICATION QUANTIZATION UNSUPERVISED PERSON RE-IDENTIFICATION