Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition
Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot...
Ausführliche Beschreibung
Autor*in: |
Li, Minghui [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
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2023 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 35(2023), 28 vom: 05. Aug., Seite 21275-21290 |
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Übergeordnetes Werk: |
volume:35 ; year:2023 ; number:28 ; day:05 ; month:08 ; pages:21275-21290 |
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DOI / URN: |
10.1007/s00521-023-08787-4 |
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Katalog-ID: |
OLC2145281797 |
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10.1007/s00521-023-08787-4 doi (DE-627)OLC2145281797 (DE-He213)s00521-023-08787-4-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Minghui verfasserin aut Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. Fine-grained classification Few-shot learning Visual attention Batch Nuclear-norm maximization Yao, Hongxun aut Wang, Yong aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 28 vom: 05. Aug., Seite 21275-21290 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:28 day:05 month:08 pages:21275-21290 https://doi.org/10.1007/s00521-023-08787-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 28 05 08 21275-21290 |
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10.1007/s00521-023-08787-4 doi (DE-627)OLC2145281797 (DE-He213)s00521-023-08787-4-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Minghui verfasserin aut Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. Fine-grained classification Few-shot learning Visual attention Batch Nuclear-norm maximization Yao, Hongxun aut Wang, Yong aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 28 vom: 05. Aug., Seite 21275-21290 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:28 day:05 month:08 pages:21275-21290 https://doi.org/10.1007/s00521-023-08787-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 28 05 08 21275-21290 |
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10.1007/s00521-023-08787-4 doi (DE-627)OLC2145281797 (DE-He213)s00521-023-08787-4-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Minghui verfasserin aut Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. Fine-grained classification Few-shot learning Visual attention Batch Nuclear-norm maximization Yao, Hongxun aut Wang, Yong aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 28 vom: 05. Aug., Seite 21275-21290 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:28 day:05 month:08 pages:21275-21290 https://doi.org/10.1007/s00521-023-08787-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 28 05 08 21275-21290 |
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10.1007/s00521-023-08787-4 doi (DE-627)OLC2145281797 (DE-He213)s00521-023-08787-4-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Minghui verfasserin aut Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. Fine-grained classification Few-shot learning Visual attention Batch Nuclear-norm maximization Yao, Hongxun aut Wang, Yong aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 28 vom: 05. Aug., Seite 21275-21290 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:28 day:05 month:08 pages:21275-21290 https://doi.org/10.1007/s00521-023-08787-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 28 05 08 21275-21290 |
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10.1007/s00521-023-08787-4 doi (DE-627)OLC2145281797 (DE-He213)s00521-023-08787-4-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Minghui verfasserin aut Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. Fine-grained classification Few-shot learning Visual attention Batch Nuclear-norm maximization Yao, Hongxun aut Wang, Yong aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 28 vom: 05. Aug., Seite 21275-21290 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:28 day:05 month:08 pages:21275-21290 https://doi.org/10.1007/s00521-023-08787-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 28 05 08 21275-21290 |
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focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition |
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Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition |
abstract |
Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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title_short |
Focus nuance and toward diversity: exploring domain-specific fine-grained few-shot recognition |
url |
https://doi.org/10.1007/s00521-023-08787-4 |
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up_date |
2024-07-04T02:36:17.588Z |
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