Generating generalized zero-shot learning based on dual-path feature enhancement
Abstract Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chang, Xinyi [verfasserIn] Wang, Zhen [verfasserIn] Liu, Wenhao [verfasserIn] Gao, Limeng [verfasserIn] Yan, Bingshuai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
Generalized zero-shot learning |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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: Multimedia systems - Springer Berlin Heidelberg, 1993, 30(2024), 5 vom: 19. Sept. |
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| Übergeordnetes Werk: |
volume:30 ; year:2024 ; number:5 ; day:19 ; month:09 |
| Links: |
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| DOI / URN: |
10.1007/s00530-024-01485-8 |
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| Katalog-ID: |
SPR05738004X |
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| 520 | |a Abstract Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. | ||
| 650 | 4 | |a Generalized zero-shot learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Variational autoencoders |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Generative adversarial networks |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feature enhancement |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wang, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Wenhao |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Limeng |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Bingshuai |e verfasserin |4 aut | |
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10.1007/s00530-024-01485-8 doi (DE-627)SPR05738004X (SPR)s00530-024-01485-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.87 bkl Chang, Xinyi verfasserin aut Generating generalized zero-shot learning based on dual-path feature enhancement 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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 Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. Generalized zero-shot learning (dpeaa)DE-He213 Variational autoencoders (dpeaa)DE-He213 Generative adversarial networks (dpeaa)DE-He213 Feature enhancement (dpeaa)DE-He213 Wang, Zhen verfasserin aut Liu, Wenhao verfasserin aut Gao, Limeng verfasserin aut Yan, Bingshuai verfasserin aut Enthalten in Multimedia systems Springer Berlin Heidelberg, 1993 30(2024), 5 vom: 19. Sept. (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:30 year:2024 number:5 day:19 month:09 https://dx.doi.org/10.1007/s00530-024-01485-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_72 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_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_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_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 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_4598 GBV_ILN_4700 54.87 VZ AR 30 2024 5 19 09 |
| spelling |
10.1007/s00530-024-01485-8 doi (DE-627)SPR05738004X (SPR)s00530-024-01485-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.87 bkl Chang, Xinyi verfasserin aut Generating generalized zero-shot learning based on dual-path feature enhancement 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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 Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. Generalized zero-shot learning (dpeaa)DE-He213 Variational autoencoders (dpeaa)DE-He213 Generative adversarial networks (dpeaa)DE-He213 Feature enhancement (dpeaa)DE-He213 Wang, Zhen verfasserin aut Liu, Wenhao verfasserin aut Gao, Limeng verfasserin aut Yan, Bingshuai verfasserin aut Enthalten in Multimedia systems Springer Berlin Heidelberg, 1993 30(2024), 5 vom: 19. Sept. (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:30 year:2024 number:5 day:19 month:09 https://dx.doi.org/10.1007/s00530-024-01485-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_72 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_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_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_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 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_4598 GBV_ILN_4700 54.87 VZ AR 30 2024 5 19 09 |
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10.1007/s00530-024-01485-8 doi (DE-627)SPR05738004X (SPR)s00530-024-01485-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.87 bkl Chang, Xinyi verfasserin aut Generating generalized zero-shot learning based on dual-path feature enhancement 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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 Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. Generalized zero-shot learning (dpeaa)DE-He213 Variational autoencoders (dpeaa)DE-He213 Generative adversarial networks (dpeaa)DE-He213 Feature enhancement (dpeaa)DE-He213 Wang, Zhen verfasserin aut Liu, Wenhao verfasserin aut Gao, Limeng verfasserin aut Yan, Bingshuai verfasserin aut Enthalten in Multimedia systems Springer Berlin Heidelberg, 1993 30(2024), 5 vom: 19. Sept. (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:30 year:2024 number:5 day:19 month:09 https://dx.doi.org/10.1007/s00530-024-01485-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_72 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_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_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_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 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_4598 GBV_ILN_4700 54.87 VZ AR 30 2024 5 19 09 |
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10.1007/s00530-024-01485-8 doi (DE-627)SPR05738004X (SPR)s00530-024-01485-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.87 bkl Chang, Xinyi verfasserin aut Generating generalized zero-shot learning based on dual-path feature enhancement 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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 Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. Generalized zero-shot learning (dpeaa)DE-He213 Variational autoencoders (dpeaa)DE-He213 Generative adversarial networks (dpeaa)DE-He213 Feature enhancement (dpeaa)DE-He213 Wang, Zhen verfasserin aut Liu, Wenhao verfasserin aut Gao, Limeng verfasserin aut Yan, Bingshuai verfasserin aut Enthalten in Multimedia systems Springer Berlin Heidelberg, 1993 30(2024), 5 vom: 19. Sept. (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:30 year:2024 number:5 day:19 month:09 https://dx.doi.org/10.1007/s00530-024-01485-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_72 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_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_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_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 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_4598 GBV_ILN_4700 54.87 VZ AR 30 2024 5 19 09 |
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10.1007/s00530-024-01485-8 doi (DE-627)SPR05738004X (SPR)s00530-024-01485-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.87 bkl Chang, Xinyi verfasserin aut Generating generalized zero-shot learning based on dual-path feature enhancement 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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 Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. Generalized zero-shot learning (dpeaa)DE-He213 Variational autoencoders (dpeaa)DE-He213 Generative adversarial networks (dpeaa)DE-He213 Feature enhancement (dpeaa)DE-He213 Wang, Zhen verfasserin aut Liu, Wenhao verfasserin aut Gao, Limeng verfasserin aut Yan, Bingshuai verfasserin aut Enthalten in Multimedia systems Springer Berlin Heidelberg, 1993 30(2024), 5 vom: 19. Sept. (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:30 year:2024 number:5 day:19 month:09 https://dx.doi.org/10.1007/s00530-024-01485-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_72 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_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_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_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 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_4598 GBV_ILN_4700 54.87 VZ AR 30 2024 5 19 09 |
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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">SPR05738004X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241030064906.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240920s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00530-024-01485-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR05738004X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00530-024-01485-8-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="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chang, Xinyi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Generating generalized zero-shot learning based on dual-path feature enhancement</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</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 GmbH Germany, part of Springer Nature 2024. 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generalized zero-shot learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variational autoencoders</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generative adversarial networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature enhancement</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Zhen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Wenhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Limeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Bingshuai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia systems</subfield><subfield code="d">Springer Berlin Heidelberg, 1993</subfield><subfield code="g">30(2024), 5 vom: 19. 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Chang, Xinyi |
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Chang, Xinyi Wang, Zhen Liu, Wenhao Gao, Limeng Yan, Bingshuai |
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Chang, Xinyi |
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generating generalized zero-shot learning based on dual-path feature enhancement |
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Generating generalized zero-shot learning based on dual-path feature enhancement |
| abstract |
Abstract Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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 Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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 Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features’ discriminative and semantic relevance from both local and global perspectives. To focus on the image’s local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network’s middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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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|
| score |
7.170065 |