Unsupervised Person Re-Identification

26 papers with code • 8 benchmarks • 3 datasets

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Latest papers with code

ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification

chenhao2345/ICE 30 Mar 2021

The main idea of instance contrastive learning is to match a same instance in different augmented views.

Unsupervised Person Re-Identification Unsupervised Representation Learning

30 Mar 2021

Cluster Contrast for Unsupervised Person Re-Identification

alibaba/cluster-contrast-reid 22 Mar 2021

We demonstrate that the inconsistency problem for cluster feature representation can be solved by the cluster-level memory dictionary. By straightforwardly applying Cluster Contrast to a standard unsupervised re-ID pipeline, it achieves considerable improvements of 9. 5%, 7. 5%, 6. 6% compared to state-of-the-art purely unsupervised re-ID methods and 5. 1%, 4. 0%, 6. 5% mAP compared to the state-of-the-art unsupervised domain adaptation re-ID methods on the Market, Duke, andMSMT17 datasets. Our source code is available at https://github. com/alibaba/cluster-contrast.

Clustering Unsupervised Domain Adaptation +1

22 Mar 2021

Intra-Inter Camera Similarity for Unsupervised Person Re-Identification

SY-Xuan/IICS 22 Mar 2021

The second stage considers the classification scores of each sample on different cameras as a new feature vector.

Transfer Learning Unsupervised Person Re-Identification

22 Mar 2021

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification

FlyingRoastDuck/MetaCam_DSCE 8 Mar 2021

This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data.

Clustering Meta-Learning +1

08 Mar 2021

Camera-aware Proxies for Unsupervised Person Re-Identification

Terminator8758/CAP-master 19 Dec 2020

These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning.

Clustering Unsupervised Person Re-Identification

19 Dec 2020

Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification

chenhao2345/ABMT 27 Nov 2020

The objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without labor-intensive identity annotations.

Unsupervised Domain Adaptation Unsupervised Person Re-Identification

27 Nov 2020

Domain Adaptive Person Re-Identification via Coupling Optimization

liu-xb/DIM_GLO 6 Nov 2020

Extensive experiments on three large-scale datasets, i. e., Market-1501, DukeMTMC-reID, and MSMT17, show that our coupling optimization outperforms state-of-the-art methods by a large margin.

Domain Adaptive Person Re-Identification Transfer Learning +1

06 Nov 2020

Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification

ece-unl-images-lab/group-attention-module-person-re-id 3 Nov 2020

Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning.

Clustering Transfer Learning +1

03 Nov 2020