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Domain Adaptation through Synthesis for Unsupervised Person Re-identification
[article]
2018
arXiv
pre-print
Preserved Fulltext
Drastic variations in illumination across surveillance cameras make the person re-identification problem extremely challenging. ...
To achieve better accuracy in unseen illumination conditions we propose a novel domain adaptation technique that takes advantage of our synthetic data and performs fine-tuning in a completely unsupervised ...
-We improve re-identification accuracy in an unsupervised fashion using a novel three-step domain adaptation technique. ...
arXiv:1804.10094v1
fatcat:lenvoegct5bqfa554drwv7strq
Domain Adaptation Through Synthesis for Unsupervised Person Re-identification
[chapter]
2018
Lecture Notes in Computer Science
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Drastic variations in illumination across surveillance cameras make the person re-identification problem extremely challenging. ...
To achieve better accuracy in unseen illumination conditions we propose a novel domain adaptation technique that takes advantage of our synthetic data and performs fine-tuning in a completely unsupervised ...
-We improve re-identification accuracy in an unsupervised fashion using a novel three-step domain adaptation technique. ...
doi:10.1007/978-3-030-01261-8_12
fatcat:fdpyp2dh7vf2jfrzzwduifbeee
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 ...
This is accomplished through the introduction of: i) k-reciprocal tracklet Clustering for Unsupervised Domain Adaptation (ktCUDA) (for pseudo-label generation on target domain), and ii) Synthesized Heterogeneous ...
Figure 2 . 2 Overview of the proposed k-reciprocal tracklet Clustering for Unsupervised Domain Adaptation (ktCUDA) in person re-ID. ...
arXiv:2001.04928v1
fatcat:p3kwnyb4bnebfhwhydjq2gvwaq
Dictionary-Based Domain Adaptation Methods for the Re-identification of Faces
[chapter]
2014
Person Re-Identification
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In particular, we discuss the adaptation of dictionary-based methods for re-identification of faces. ...
Re-identification refers to the problem of recognizing a person at a different location after one has been captured by a camera at a previous location. ...
We are interested in face re-identification as face is an important biometric signature to determine the identity of a person. ...
doi:10.1007/978-1-4471-6296-4_13
dblp:series/acvpr/QiuNC14
fatcat:w5ggcnr4kzglzfgpip37iamjsq
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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The file type is application/pdf.
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 ...
This is accomplished through the introduction of: i) k-reciprocal tracklet Clustering for Unsupervised Domain Adaptation (ktCUDA) (for pseudo-label generation on target domain), and ii) Synthesized Heterogeneous ...
Figure 2 . 2 Overview of the proposed k-reciprocal tracklet Clustering for Unsupervised Domain Adaptation (ktCUDA) in person re-ID. ...
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. ...
We hope the proposed approach would inspire more work of integrating disentangling and adaptation for unsupervised cross-domain person re-id. ...
arXiv:2007.10315v1
fatcat:b63qxatktrbcpnsklapuzmuoey
Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1905.03422v1 fatcat:5roetzfozjdungeyybsxdjqmuuOther Versions
This paper alleviates this issue by proposing a simple baseline for domain generalizable (DG) person re-identification. ...
Contemporary person re-identification () methods usually require access to data from the deployment camera network during training in order to perform well. ...
Method Setup For domain generalization person re-ID, we as- [7] . ...
arXiv:1905.03422v3
fatcat:2oarv5t3dvcmnklsmb2jrm47da
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair. ...
to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain). ...
Vision Semantics Ltd, Royal Society Newton Advanced Fellowship Programme (NA150459), and In-novateUK Industrial Challenge Project on Developing and Commercialising Intelligent Video Analytics Solutions for ...
doi:10.1109/cvpr.2018.00242
dblp:conf/cvpr/WangZGL18
fatcat:6t46zlmqendb5i2fbuorvsrcw4
UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification
[article]
2020
arXiv
pre-print
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The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. ...
This offers a good basis for unsupervised domain adaption, where our pre-trained model is easily plugged into the state-of-the-art algorithms towards higher accuracy. ...
Conclusions This paper presents UnrealPerson, a novel pipeline for person re-identification (ReID). ...
arXiv:2012.04268v2
fatcat:t5euwqyoajdfndf6xr2avplyvq
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification
[article]
2018
arXiv
pre-print
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Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair. ...
to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain). ...
Vision Semantics Ltd, Royal Society Newton Advanced Fellowship Programme (NA150459), and In-novateUK Industrial Challenge Project on Developing and Commercialising Intelligent Video Analytics Solutions for ...
arXiv:1803.09786v1
fatcat:zgdruvntp5f7feds5czgfvh7wa
Unsupervised Tracklet Person Re-Identification
[article]
2019
arXiv
pre-print
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Extensive experiments demonstrate the superiority of the proposed model over the state-of-the-art unsupervised learning and domain adaptation person re-id methods on eight benchmarking datasets. ...
Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. ...
RELATED WORK Person Re-Identification. ...
arXiv:1903.00535v1
fatcat:b2y3min6ezhwrayhvtvo2pf27m
Unsupervised Tracklet Person Re-Identification
2019
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preserved Fulltext
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Extensive experiments demonstrate the superiority of the proposed model over the state-of-the-art unsupervised learning and domain adaptation person re-id methods on eight benchmarking datasets. ...
Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. ...
RELATED WORK Person Re-Identification. ...
doi:10.1109/tpami.2019.2903058
pmid:30843803
fatcat:sdwp2cebhfhw3aszo5jptwzlkm
Instance-Guided Context Rendering for Cross-Domain Person Re-Identification
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Preserved Fulltext
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Existing person re-identification (re-id) methods mostly assume the availability of large-scale identity labels for model learning in any target domain deployment. ...
Unlike previous image synthesis methods that transform the source person images into limited fixed target styles, our approach produces more visually plausible, and diverse synthetic training data. ...
Related Work Unsupervised Cross-Domain Person Re-Identification aims to transfer the identity discriminative knowledge from a labelled source domain to an unlabelled target domain. ...
doi:10.1109/iccv.2019.00032
dblp:conf/iccv/ChenZG19
fatcat:2ehie77suzbnjpa7hhgzwwwcce
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
Unsupervised clothing change adaptive person ReID
[article]
2021
arXiv
pre-print
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We design a novel unsupervised model, Sync-Person-Cloud ReID, to solve the unsupervised clothing change person ReID problem. ...
For the last challenge, some researchers try to make model learn information from a labeled dataset as a source to an unlabeled dataset. Whereas purely unsupervised training is less used. ...
Introduction Person re-identification (ReID) (Ye et al. 2021) is designed to match specific pedestrians in images or video sequences. ...
arXiv:2109.03702v2
fatcat:m37puuo2tfanbidhgh4pluzpnu
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