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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3122444
|2 doi
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|a pubmed24n1107.xml
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|a (DE-627)NLM332393917
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|a eng
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| 100 |
1 |
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|a Eom, Chanho
|e verfasserin
|4 aut
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| 245 |
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|a Disentangled Representations for Short-Term and Long-Term Person Re-Identification
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 09.11.2022
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|a Date Revised 19.11.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons could have the same attribute, and persons' appearances look different, e.g., with viewpoint changes. Recent reID methods focus on learning person features discriminative only for a particular factor of variations (e.g., human pose), which also requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to factorize person images into identity-related and -unrelated features. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose). To this end, we propose a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN). It disentangles identity-related and -unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals. We restrict the distribution of identity-unrelated features, or encourage the identity-related and -unrelated features to be uncorrelated, facilitating the disentanglement process. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks, including Market-1501, CUHK03 and DukeMTMC-reID. We further demonstrate the advantages of disentangling person representations on a long-term reID task, setting a new state of the art on a Celeb-reID dataset. Our code and models are available online: https://cvlab-yonsei.github.io/projects/ISGAN/
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Lee, Wonkyung
|e verfasserin
|4 aut
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| 700 |
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|a Lee, Geon
|e verfasserin
|4 aut
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| 700 |
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|a Ham, Bumsub
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 12 vom: 26. Dez., Seite 8975-8991
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:44
|g year:2022
|g number:12
|g day:26
|g month:12
|g pages:8975-8991
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|u http://dx.doi.org/10.1109/TPAMI.2021.3122444
|3 Volltext
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