Collaborative learning mutual network for domain adaptation in person re-identification
Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tay, Chiat-Pin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2022), 14 vom: 16. März, Seite 12211-12222 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:14 ; day:16 ; month:03 ; pages:12211-12222 |
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| DOI / URN: |
10.1007/s00521-022-07108-5 |
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| Katalog-ID: |
SPR047594225 |
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| 520 | |a Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. | ||
| 650 | 4 | |a Collaborative and mutual learning |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Contrastive learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yap, Kim-Hui |4 aut | |
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10.1007/s00521-022-07108-5 doi (DE-627)SPR047594225 (SPR)s00521-022-07108-5-e DE-627 ger DE-627 rakwb eng Tay, Chiat-Pin verfasserin (orcid)0000-0002-4984-9780 aut Collaborative learning mutual network for domain adaptation in person re-identification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. Collaborative and mutual learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Person re-identification (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Yap, Kim-Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 14 vom: 16. März, Seite 12211-12222 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:14 day:16 month:03 pages:12211-12222 https://dx.doi.org/10.1007/s00521-022-07108-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 14 16 03 12211-12222 |
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10.1007/s00521-022-07108-5 doi (DE-627)SPR047594225 (SPR)s00521-022-07108-5-e DE-627 ger DE-627 rakwb eng Tay, Chiat-Pin verfasserin (orcid)0000-0002-4984-9780 aut Collaborative learning mutual network for domain adaptation in person re-identification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. Collaborative and mutual learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Person re-identification (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Yap, Kim-Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 14 vom: 16. März, Seite 12211-12222 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:14 day:16 month:03 pages:12211-12222 https://dx.doi.org/10.1007/s00521-022-07108-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 14 16 03 12211-12222 |
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10.1007/s00521-022-07108-5 doi (DE-627)SPR047594225 (SPR)s00521-022-07108-5-e DE-627 ger DE-627 rakwb eng Tay, Chiat-Pin verfasserin (orcid)0000-0002-4984-9780 aut Collaborative learning mutual network for domain adaptation in person re-identification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. Collaborative and mutual learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Person re-identification (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Yap, Kim-Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 14 vom: 16. März, Seite 12211-12222 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:14 day:16 month:03 pages:12211-12222 https://dx.doi.org/10.1007/s00521-022-07108-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 14 16 03 12211-12222 |
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10.1007/s00521-022-07108-5 doi (DE-627)SPR047594225 (SPR)s00521-022-07108-5-e DE-627 ger DE-627 rakwb eng Tay, Chiat-Pin verfasserin (orcid)0000-0002-4984-9780 aut Collaborative learning mutual network for domain adaptation in person re-identification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. Collaborative and mutual learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Person re-identification (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Yap, Kim-Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 14 vom: 16. März, Seite 12211-12222 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:14 day:16 month:03 pages:12211-12222 https://dx.doi.org/10.1007/s00521-022-07108-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 14 16 03 12211-12222 |
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10.1007/s00521-022-07108-5 doi (DE-627)SPR047594225 (SPR)s00521-022-07108-5-e DE-627 ger DE-627 rakwb eng Tay, Chiat-Pin verfasserin (orcid)0000-0002-4984-9780 aut Collaborative learning mutual network for domain adaptation in person re-identification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. Collaborative and mutual learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Person re-identification (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Yap, Kim-Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 14 vom: 16. März, Seite 12211-12222 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:14 day:16 month:03 pages:12211-12222 https://dx.doi.org/10.1007/s00521-022-07108-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 14 16 03 12211-12222 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR047594225</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509103713.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220716s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-022-07108-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR047594225</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00521-022-07108-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tay, Chiat-Pin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4984-9780</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Collaborative learning mutual network for domain adaptation in person re-identification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). 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Tay, Chiat-Pin |
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collaborative learning mutual network for domain adaptation in person re-identification |
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Collaborative learning mutual network for domain adaptation in person re-identification |
| abstract |
Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstractGer |
Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract In this paper, we propose a new Collaborative Learning Mutual Network (CLM-Net) for domain adaptation in person re-identification (re-id). Current state-of-the-art re-id models achieved good performances when trained on published datasets. However, these trained models work poorly on newly collected dataset for target domain. Our proposed CLM-Net aims to overcome this limitation by using body part and saliency map learning to improve the discriminative representations in a deep ensemble framework. Specifically, we integrate body part features learning tasks and a global saliency task to the baseline model so that information that helps to identify the pedestrian can be extracted. As a result, the trained representations produced better pseudo labels during the clustering process and provide smooth transition from the source to the target domains. We also propose to leverage the unlabeled data by using contrastive learning to further encode strong representations. Our proposed CLM-Net outperforms most of the current state-of-the-art, with 80.9%, 69.7%, 29.0% and 26.6% mAP accuracy on Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT domain adaptation, respectively, using ResNet-50 backbone. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| title_short |
Collaborative learning mutual network for domain adaptation in person re-identification |
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https://dx.doi.org/10.1007/s00521-022-07108-5 |
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Yap, Kim-Hui |
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Yap, Kim-Hui |
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10.1007/s00521-022-07108-5 |
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2024-07-03T13:45:07.159Z |
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| score |
7.401458 |