Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Zheng, Yi; Tang, Shixiang; Teng, Guolong; Ge, Yixiao; Liu, Kaijian; Qin, Jing; Qi, Donglian; Chen, Dapeng

doi:10.1109/iccv48922.2021.00826

Cited by 77 publications

(23 citation statements)

References 44 publications

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“…An optimization strategy was presented in [233] to achieve the generalization. Specifically label banks were formed in hierarchical manner, mini-batches were updated using iterative approach.…”

Section: Cnn-based Approachesmentioning

confidence: 99%

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Zahra¹,

Perwaiz²,

Shahzad³

et al. 2022

Preprint

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Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views. Owing to its potential in various applications and research significance, a plethora of deep learning based re-Id approaches have been proposed in the recent years. However, there exist several vision related challenges, e.g., occlusion, pose scale & viewpoint variance, background clutter, person misalignment and cross-domain generalization across camera modalities, which makes the problem of re-Id still far from being solved. Majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. In this context, a comprehensive review of current re-ID approaches in solving theses challenges is needed to analyze and focus on particular aspects for further advancements. At present, such a focused review does not exist and henceforth in this paper, we have presented a systematic challenge-specific literature survey of 230+ papers between the years of 2015-21. 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. Moreover, we have presented several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing person re-Id research and eventually help to develop practical real world solutions. Scope/Objective of the ReviewIn this paper, we have targeted the most popular challenges in person re-id to perform systematic challenge-wise review of the published approaches. In this context, we have collected papers from top conferences and journals for the years from 2015 to 2021. The progress in papers addressing each challenge and its influence on published results is

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Section: Cnn-based Approachesmentioning

confidence: 99%

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Zahra¹,

Perwaiz²,

Shahzad³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the pseudo labels generated by an offline clustering technique such as DBSCAN [13] inevitably contain noise. In order to reduce the label noise, various methods [14], [16], [18]- [20] have been developed recently. For instance, RLCC [16] refines pseudo labels via clustering consensus over consecutive training generations.…”

Section: B Pseudo Label Refinementmentioning

confidence: 99%

“…It implies that the generated pseudo labels might be out of date. To deal with the inevitable label noise, efforts have been made in label refinement [14]- [16], hybrid contrastive learning [10], [12], [17] that combines cluster-and instance-level contrasts together, online pseudo label generation [18], and other techniques [19], [20].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, contrastive learning (CL) [7]- [9] has attracted a surge of research interest. Various CL-based methods [6], [10]- [12], [18], [31], [35] have been developed for unsupervised Re-ID. Moreover, the issue of pseudo label noise occurred in unsupervised Re-ID has also been noticed recently and various methods [14], [16], [18]- [20] have been proposed to address it.…”

Section: Introductionmentioning

confidence: 99%

“…Various CL-based methods [6], [10]- [12], [18], [31], [35] have been developed for unsupervised Re-ID. Moreover, the issue of pseudo label noise occurred in unsupervised Re-ID has also been noticed recently and various methods [14], [16], [18]- [20] have been proposed to address it. We will introduce these related work and state the difference of our work in Section II-B and II-D.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification

Wang¹,

Li²,

Lai³

et al. 2022

Preprint

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Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsupervised Re-ID is clustering-based, which generates pseudo labels by clustering and uses the pseudo labels to train a Re-ID model iteratively. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the intra-cluster variance mainly caused by the change of cameras. To address this issue, we propose to split each single cluster into multiple proxies according to camera views. The camera-aware proxies explicitly capture local structures within clusters, by which the intra-ID variance and inter-ID similarity can be better tackled. Assisted with the camera-aware proxies, we design two proxylevel contrastive learning losses that are, respectively, based on offline and online association results. The offline association directly associates proxies according to the clustering and splitting results, while the online strategy dynamically associates proxies in terms of up-to-date features to reduce the noise caused by the delayed update of pseudo labels. The combination of two losses enable us to train a desirable Re-ID model. Extensive experiments on three person Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach demonstrates competitive performance with state-of-the-art methods. Code will be available at: https://github.com/Terminator8758/O2CAP.

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Dynamically Transformed Instance Normalization Network for Generalizable Person Re-Identification

Jiao

Liu

Gao

et al. 2022

Lecture Notes in Computer Science

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Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Cited by 77 publications

References 44 publications

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification

Dynamically Transformed Instance Normalization Network for Generalizable Person Re-Identification

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