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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3308393
|2 doi
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|a pubmed24n1204.xml
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Jia, Chengyou
|e verfasserin
|4 aut
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| 245 |
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|a Collaborative Contrastive Refining for Weakly Supervised Person Search
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 07.09.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Weakly supervised person search involves training a model with only bounding box annotations, without human-annotated identities. Clustering algorithms are commonly used to assign pseudo-labels to facilitate this task. However, inaccurate pseudo-labels and imbalanced identity distributions can result in severe label and sample noise. In this work, we propose a novel Collaborative Contrastive Refining (CCR) weakly-supervised framework for person search that jointly refines pseudo-labels and the sample-learning process with different contrastive strategies. Specifically, we adopt a hybrid contrastive strategy that leverages both visual and context clues to refine pseudo-labels, and leverage the sample-mining and noise-contrastive strategy to reduce the negative impact of imbalanced distributions by distinguishing positive samples and noise samples. Our method brings two main advantages: 1) it facilitates better clustering results for refining pseudo-labels by exploring the hybrid similarity; 2) it is better at distinguishing query samples and noise samples for refining the sample-learning process. Extensive experiments demonstrate the superiority of our approach over the state-of-the-art weakly supervised methods by a large margin (more than 3% mAP on CUHK-SYSU). Moreover, by leveraging more diverse unlabeled data, our method achieves comparable or even better performance than the state-of-the-art supervised methods
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|a Journal Article
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| 700 |
1 |
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|a Luo, Minnan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Caixia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Linchao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chang, Xiaojun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zheng, Qinghua
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 29., Seite 4951-4963
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:32
|g year:2023
|g day:29
|g pages:4951-4963
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2023.3308393
|3 Volltext
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|d 32
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