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Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis
[article]
2020
arXiv
pre-print
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In this paper, we address the aforementioned challenges faced in person re-identification for real-world, practical scenarios by introducing a novel, unsupervised domain adaptation approach for person ...
While recent success has been achieved via supervised learning using deep neural networks, such methods have limited widespread adoption due to the need for large-scale, customized data annotation. ...
Conclusion In this work, we presented new strategies for unsupervised person re-ID using unlabelled data from a target domain. ...
arXiv:2001.04928v1
fatcat:p3kwnyb4bnebfhwhydjq2gvwaq
Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
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In this paper, we address the aforementioned challenges faced in person re-identification for real-world, practical scenarios by introducing a novel, unsupervised domain adaptation approach for person ...
While recent success has been achieved via supervised learning using deep neural networks, such methods have limited widespread adoption due to the need for large-scale, customized data annotation. ...
Conclusion In this work, we presented new strategies for unsupervised person re-ID using unlabelled data from a target domain. ...
doi:10.1109/wacv45572.2020.9093606
dblp:conf/wacv/KumarSMW20
fatcat:qcdsyzsbsfe5fgvz7dc73jxejy
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
[article]
2020
arXiv
pre-print
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Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. ...
Recently, there has been a growing interest in using unsupervised domain adaptation to address this scalability issue. ...
First, we propose a joint learning framework for unsupervised cross-domain person re-id to disentangle id-related/unrelated factors so that adaptation can be more effectively performed on the id-related ...
arXiv:2007.10315v1
fatcat:b63qxatktrbcpnsklapuzmuoey
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions
[article]
2021
arXiv
pre-print
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Therefore, unsupervised person Re-ID has drawn increasing attention for its potential to address the scalability issue in person Re-ID. ...
Unsupervised person Re-ID is challenging primarily due to lacking identity labels to supervise person feature learning. ...
Unsupervised cross-domain person re-identification In Unsupervised cross-domain person Re-Id, a labelled source domain and an unlabeled target domain are used to train a Re-ID model for the target domain ...
arXiv:2109.06057v2
fatcat:epfow7w3trevff5iku2uvb4ov4
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
[article]
2020
arXiv
pre-print
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in autonomous systems, including image style transfer, image superresolution, image deblurring/dehazing/rain removal, semantic segmentation, depth estimation, pedestrian detection and person re-identification ...
(re-ID). ...
Person re-identification. A similar while more difficult task than pedestrian detection, person re-identification (re-ID), requires matching pedestrians in disjoint camera views. ...
arXiv:2003.12948v3
fatcat:qtmjs74p2vh6thdotbhgebdvoi
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2112.06632v1 fatcat:jg4swm4365fpra6iveazdmzi4i
Unsupervised domain adaptive person re-identification (ReID) has been extensively investigated to mitigate the adverse effects of domain gaps. ...
In this paper, to address more practical scenarios, we propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID. ...
Our contributions can be summarized in three aspects:
Related Works
Domain Adaptive Person Re-identification Person re-identification (ReID) has been widely investigated and applied in many real-world ...
arXiv:2112.06632v2
fatcat:sninsegzbjhkrcw6vmmr22mxaq
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
2020
Patterns
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re-identification. ...
Finally, we discuss several challenges and future topics for the use of adversarial learning, RL, and meta-learning in autonomous systems. ...
ACKNOWLEDGMENTS The authors would like to thank the Editor-in-Chief, Scientific Editor, and anonymous referees for their helpful comments and suggestions, which have greatly improved this paper. ...
doi:10.1016/j.patter.2020.100050
pmid:33205114
pmcid:PMC7660378
fatcat:vs7wm2yrwjamjbaml36663wvze
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
[article]
2020
arXiv
pre-print
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Domain adaptive object re-ID aims to transfer the learned knowledge from the labeled source domain to the unlabeled target domain to tackle the open-class re-identification problems. ...
The hybrid memory dynamically generates source-domain class-level, target-domain cluster-level and un-clustered instance-level supervisory signals for learning feature representations. ...
(a) Real→real adaptation on person re-ID datasets. Synthetic→real adaptation on person re-ID datasets. ...
arXiv:2006.02713v2
fatcat:o3p2qjowcvaudce2twtxpok7fe
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
[article]
2021
arXiv
pre-print
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Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. ...
State-of-the-art object Re-ID approaches adopt clustering algorithms to generate pseudo-labels for the unlabeled target domain. ...
Related Works We review the literature in three parts: 1) unsupervised domain adaptive (UDA) object Re-ID, 2) contrastive learning, and 3) deep learning with noisy labels. ...
arXiv:2108.09682v1
fatcat:7kxdkzzf3fevfdcxjmt6eox3nu
Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends
[article]
2022
arXiv
pre-print
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Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. ...
For the first time a survey of this type have been presented where the person re-Id approaches are reviewed in such solution-oriented perspective. ...
In [224] domain adaptive person re-id was proposed using unsupervised target domain. Multiple expert brainstorming networks were learned with multiple architectures for optimal re-id. ...
arXiv:2202.13121v1
fatcat:luwwbcwspndqpauj4dosmmojee
Deep Visual Domain Adaptation: A Survey
[article]
2018
arXiv
pre-print
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Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data. ...
There have been comprehensive surveys for shallow domain adaption, but few timely reviews the emerging deep learning based methods. ...
Person Re-identification In the community, person re-identification (re-ID) has become increasingly popular. ...
arXiv:1802.03601v4
fatcat:d5hwwecipjfjzmh7725lmepzfe
VTBR: Semantic-based Pretraining for Person Re-Identification
[article]
2021
arXiv
pre-print
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However, recent works show a surprising result that ImageNet pretraining has limited impacts on Re-ID system due to the large domain gap between ImageNet and person Re-ID data. ...
Generally, supervised ImageNet pretraining is commonly used to initialize the backbones of person re-identification (Re-ID) models. ...
Unsupervised Domain Adaption Our semantic-based pretraining method enjoys the benefits of flexible corner scenarios of domain adaptive Re-ID tasks, where labelled data in target domain is hard to obtain ...
arXiv:2110.05074v1
fatcat:nw2qu5ozlvhbzmmlqitloonaxy
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. ...
., bidirectional multi-scale feature re-projection loss and correlation consistency loss, to help translate all synthetic stereo images into realistic ones as well as maintain epipolar constraints. ...
segmentation, person re-identification and object detection [15, 41, 32, 3] . ...
doi:10.1109/cvpr42600.2020.01277
dblp:conf/cvpr/LiuYSWL20
fatcat:7r6mbp5y3fa2dp42gzyblssmpe
Domain-Adversarial Training of Neural Networks
[article]
2016
arXiv
pre-print
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We also validate the approach for descriptor learning task in the context of person re-identification application. ...
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. ...
We also thank the Graphics & Media Lab, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University for providing the synthetic road signs data set. ...
arXiv:1505.07818v4
fatcat:xj7camuj3fagdmfgqqf2mrnmb4
Domain-Adversarial Training of Neural Networks
[chapter]
2017
Advances in Computer Vision and Pattern Recognition
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We also validate the approach for descriptor learning task in the context of person re-identification application. ...
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. ...
We also thank the Graphics & Media Lab, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University for providing the synthetic road signs data set. ...
doi:10.1007/978-3-319-58347-1_10
fatcat:ysrh2ae6ajfi7jogffieaa7khy
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