Coarse2Fine: a two-stage training method for fine-grained visual classification
Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Eshratifar, Amir Erfan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Fine-grained visual classification |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Machine vision and applications - Springer Berlin Heidelberg, 1988, 32(2021), 2 vom: 25. Feb. |
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volume:32 ; year:2021 ; number:2 ; day:25 ; month:02 |
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10.1007/s00138-021-01180-y |
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| 520 | |a Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets. | ||
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| 650 | 4 | |a Visual attention networks | |
| 700 | 1 | |a Eigen, David |4 aut | |
| 700 | 1 | |a Gormish, Michael |4 aut | |
| 700 | 1 | |a Pedram, Massoud |4 aut | |
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10.1007/s00138-021-01180-y doi (DE-627)OLC2123987204 (DE-He213)s00138-021-01180-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Eshratifar, Amir Erfan verfasserin (orcid)0000-0002-1339-7671 aut Coarse2Fine: a two-stage training method for fine-grained visual classification 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets. Fine-grained visual classification Weakly supervised object localization Visual attention networks Eigen, David aut Gormish, Michael aut Pedram, Massoud aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 32(2021), 2 vom: 25. Feb. (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:32 year:2021 number:2 day:25 month:02 https://doi.org/10.1007/s00138-021-01180-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2021 2 25 02 |
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10.1007/s00138-021-01180-y doi (DE-627)OLC2123987204 (DE-He213)s00138-021-01180-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Eshratifar, Amir Erfan verfasserin (orcid)0000-0002-1339-7671 aut Coarse2Fine: a two-stage training method for fine-grained visual classification 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets. Fine-grained visual classification Weakly supervised object localization Visual attention networks Eigen, David aut Gormish, Michael aut Pedram, Massoud aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 32(2021), 2 vom: 25. Feb. (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:32 year:2021 number:2 day:25 month:02 https://doi.org/10.1007/s00138-021-01180-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2021 2 25 02 |
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10.1007/s00138-021-01180-y doi (DE-627)OLC2123987204 (DE-He213)s00138-021-01180-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Eshratifar, Amir Erfan verfasserin (orcid)0000-0002-1339-7671 aut Coarse2Fine: a two-stage training method for fine-grained visual classification 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets. Fine-grained visual classification Weakly supervised object localization Visual attention networks Eigen, David aut Gormish, Michael aut Pedram, Massoud aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 32(2021), 2 vom: 25. Feb. (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:32 year:2021 number:2 day:25 month:02 https://doi.org/10.1007/s00138-021-01180-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2021 2 25 02 |
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Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
| abstractGer |
Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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Abstract Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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Coarse2Fine: a two-stage training method for fine-grained visual classification |
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Eigen, David Gormish, Michael Pedram, Massoud |
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10.1007/s00138-021-01180-y |
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