MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers
Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structur...
Ausführliche Beschreibung
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| Autor*in: |
Wang, Rui [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 Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 54(2022), 5 vom: 29. März, Seite 3941-3964 |
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| Übergeordnetes Werk: |
volume:54 ; year:2022 ; number:5 ; day:29 ; month:03 ; pages:3941-3964 |
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| DOI / URN: |
10.1007/s11063-022-10794-w |
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SPR048359742 |
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| 520 | |a Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. | ||
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| 700 | 1 | |a Geng, Fudi |4 aut | |
| 700 | 1 | |a Wang, Xiangyang |0 (orcid)0000-0003-1394-6068 |4 aut | |
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10.1007/s11063-022-10794-w doi (DE-627)SPR048359742 (SPR)s11063-022-10794-w-e DE-627 ger DE-627 rakwb eng Wang, Rui verfasserin aut MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. Human pose estimation (dpeaa)DE-He213 High-resolution networks (dpeaa)DE-He213 Multi-scale transformers (dpeaa)DE-He213 Multi-scale self-attention (dpeaa)DE-He213 Geng, Fudi aut Wang, Xiangyang (orcid)0000-0003-1394-6068 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 5 vom: 29. März, Seite 3941-3964 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:5 day:29 month:03 pages:3941-3964 https://dx.doi.org/10.1007/s11063-022-10794-w 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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 54 2022 5 29 03 3941-3964 |
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10.1007/s11063-022-10794-w doi (DE-627)SPR048359742 (SPR)s11063-022-10794-w-e DE-627 ger DE-627 rakwb eng Wang, Rui verfasserin aut MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. Human pose estimation (dpeaa)DE-He213 High-resolution networks (dpeaa)DE-He213 Multi-scale transformers (dpeaa)DE-He213 Multi-scale self-attention (dpeaa)DE-He213 Geng, Fudi aut Wang, Xiangyang (orcid)0000-0003-1394-6068 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 5 vom: 29. März, Seite 3941-3964 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:5 day:29 month:03 pages:3941-3964 https://dx.doi.org/10.1007/s11063-022-10794-w 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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 54 2022 5 29 03 3941-3964 |
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10.1007/s11063-022-10794-w doi (DE-627)SPR048359742 (SPR)s11063-022-10794-w-e DE-627 ger DE-627 rakwb eng Wang, Rui verfasserin aut MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. Human pose estimation (dpeaa)DE-He213 High-resolution networks (dpeaa)DE-He213 Multi-scale transformers (dpeaa)DE-He213 Multi-scale self-attention (dpeaa)DE-He213 Geng, Fudi aut Wang, Xiangyang (orcid)0000-0003-1394-6068 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 5 vom: 29. März, Seite 3941-3964 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:5 day:29 month:03 pages:3941-3964 https://dx.doi.org/10.1007/s11063-022-10794-w 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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 54 2022 5 29 03 3941-3964 |
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10.1007/s11063-022-10794-w doi (DE-627)SPR048359742 (SPR)s11063-022-10794-w-e DE-627 ger DE-627 rakwb eng Wang, Rui verfasserin aut MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. Human pose estimation (dpeaa)DE-He213 High-resolution networks (dpeaa)DE-He213 Multi-scale transformers (dpeaa)DE-He213 Multi-scale self-attention (dpeaa)DE-He213 Geng, Fudi aut Wang, Xiangyang (orcid)0000-0003-1394-6068 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 5 vom: 29. März, Seite 3941-3964 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:5 day:29 month:03 pages:3941-3964 https://dx.doi.org/10.1007/s11063-022-10794-w 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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 54 2022 5 29 03 3941-3964 |
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10.1007/s11063-022-10794-w doi (DE-627)SPR048359742 (SPR)s11063-022-10794-w-e DE-627 ger DE-627 rakwb eng Wang, Rui verfasserin aut MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. Human pose estimation (dpeaa)DE-He213 High-resolution networks (dpeaa)DE-He213 Multi-scale transformers (dpeaa)DE-He213 Multi-scale self-attention (dpeaa)DE-He213 Geng, Fudi aut Wang, Xiangyang (orcid)0000-0003-1394-6068 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 5 vom: 29. März, Seite 3941-3964 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:5 day:29 month:03 pages:3941-3964 https://dx.doi.org/10.1007/s11063-022-10794-w 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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 54 2022 5 29 03 3941-3964 |
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(in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. 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mtpose: human pose estimation with high-resolution multi-scale transformers |
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MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers |
| abstract |
Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract HRNet (High-Resolution Networks) as reported by Sun et al. (in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019) has been the state-of-the-art human pose estimation method, benefitting from its parallel high-resolution designed network structures. However, HRNet is still a typical CNN (Convolutional Neural Networks) architecture, with local convolution operations. Recently, Transformers have been successfully applied in many computer vision areas. The main mechanism in Transformers is self-attention, which can learn global or long-range dependencies among different parts. In this paper, we propose a human pose estimation framework built upon High-Resolution Multi-scale Transformers, termed MTPose. We combine the two advantages of high-resolution and Transformers together to improve the performance. Specifically, we design a sub-network, MTNet (Multi-scale Transformers-based high-resolution Networks), which consists of two parallel branches. One is high-resolution with convolutional local operations, named as local branch. The other is the global branch utilizing multi-scale Transformer encoders to learn long-range dependencies of the whole body keypoints. At the end of the networks, the two branches are integrated together to predict the final keypoint heatmaps. Experiments on two benchmark datasets, the MSCOCO keypoint detection dataset and MPII human pose dataset, demonstrate that our method can significantly improve the state-of-the-art human pose estimation methods. Code will be available at: https://github.com/fudiGeng/MTPose. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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MTPose: Human Pose Estimation with High-Resolution Multi-scale Transformers |
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https://dx.doi.org/10.1007/s11063-022-10794-w |
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Geng, Fudi Wang, Xiangyang |
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10.1007/s11063-022-10794-w |
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| score |
7.3987894 |