Semantic Contrastive Embedding for Generalized Zero-Shot Learning
Abstract Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Han, Zongyan [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. Springer Nature or its licensor 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: International journal of computer vision - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 130(2022), 11 vom: 18. Aug., Seite 2606-2622 |
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| Übergeordnetes Werk: |
volume:130 ; year:2022 ; number:11 ; day:18 ; month:08 ; pages:2606-2622 |
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| DOI / URN: |
10.1007/s11263-022-01656-y |
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SPR048259632 |
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| 520 | |a Abstract Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. | ||
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| 650 | 4 | |a Semantic embedding |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Contrastive learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Fu, Zhenyong |4 aut | |
| 700 | 1 | |a Chen, Shuo |4 aut | |
| 700 | 1 | |a Yang, Jian |4 aut | |
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10.1007/s11263-022-01656-y doi (DE-627)SPR048259632 (SPR)s11263-022-01656-y-e DE-627 ger DE-627 rakwb eng Han, Zongyan verfasserin (orcid)0000-0002-2011-5679 aut Semantic Contrastive Embedding for Generalized Zero-Shot Learning 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. Springer Nature or its licensor 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) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. Generalized zero-shot learning (dpeaa)DE-He213 Attribute (dpeaa)DE-He213 Semantic embedding (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Fu, Zhenyong aut Chen, Shuo aut Yang, Jian aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 130(2022), 11 vom: 18. Aug., Seite 2606-2622 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:130 year:2022 number:11 day:18 month:08 pages:2606-2622 https://dx.doi.org/10.1007/s11263-022-01656-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2022 11 18 08 2606-2622 |
| spelling |
10.1007/s11263-022-01656-y doi (DE-627)SPR048259632 (SPR)s11263-022-01656-y-e DE-627 ger DE-627 rakwb eng Han, Zongyan verfasserin (orcid)0000-0002-2011-5679 aut Semantic Contrastive Embedding for Generalized Zero-Shot Learning 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. Springer Nature or its licensor 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) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. Generalized zero-shot learning (dpeaa)DE-He213 Attribute (dpeaa)DE-He213 Semantic embedding (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Fu, Zhenyong aut Chen, Shuo aut Yang, Jian aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 130(2022), 11 vom: 18. Aug., Seite 2606-2622 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:130 year:2022 number:11 day:18 month:08 pages:2606-2622 https://dx.doi.org/10.1007/s11263-022-01656-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2022 11 18 08 2606-2622 |
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10.1007/s11263-022-01656-y doi (DE-627)SPR048259632 (SPR)s11263-022-01656-y-e DE-627 ger DE-627 rakwb eng Han, Zongyan verfasserin (orcid)0000-0002-2011-5679 aut Semantic Contrastive Embedding for Generalized Zero-Shot Learning 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. Springer Nature or its licensor 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) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. Generalized zero-shot learning (dpeaa)DE-He213 Attribute (dpeaa)DE-He213 Semantic embedding (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Fu, Zhenyong aut Chen, Shuo aut Yang, Jian aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 130(2022), 11 vom: 18. Aug., Seite 2606-2622 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:130 year:2022 number:11 day:18 month:08 pages:2606-2622 https://dx.doi.org/10.1007/s11263-022-01656-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2022 11 18 08 2606-2622 |
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10.1007/s11263-022-01656-y doi (DE-627)SPR048259632 (SPR)s11263-022-01656-y-e DE-627 ger DE-627 rakwb eng Han, Zongyan verfasserin (orcid)0000-0002-2011-5679 aut Semantic Contrastive Embedding for Generalized Zero-Shot Learning 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. Springer Nature or its licensor 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) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. Generalized zero-shot learning (dpeaa)DE-He213 Attribute (dpeaa)DE-He213 Semantic embedding (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Fu, Zhenyong aut Chen, Shuo aut Yang, Jian aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 130(2022), 11 vom: 18. Aug., Seite 2606-2622 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:130 year:2022 number:11 day:18 month:08 pages:2606-2622 https://dx.doi.org/10.1007/s11263-022-01656-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2022 11 18 08 2606-2622 |
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10.1007/s11263-022-01656-y doi (DE-627)SPR048259632 (SPR)s11263-022-01656-y-e DE-627 ger DE-627 rakwb eng Han, Zongyan verfasserin (orcid)0000-0002-2011-5679 aut Semantic Contrastive Embedding for Generalized Zero-Shot Learning 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. Springer Nature or its licensor 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) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. Generalized zero-shot learning (dpeaa)DE-He213 Attribute (dpeaa)DE-He213 Semantic embedding (dpeaa)DE-He213 Contrastive learning (dpeaa)DE-He213 Fu, Zhenyong aut Chen, Shuo aut Yang, Jian aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 130(2022), 11 vom: 18. Aug., Seite 2606-2622 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:130 year:2022 number:11 day:18 month:08 pages:2606-2622 https://dx.doi.org/10.1007/s11263-022-01656-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2022 11 18 08 2606-2622 |
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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">SPR048259632</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509112910.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221001s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11263-022-01656-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048259632</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11263-022-01656-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Han, Zongyan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2011-5679</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Semantic Contrastive Embedding for Generalized Zero-Shot Learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. 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semantic contrastive embedding for generalized zero-shot learning |
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Semantic Contrastive Embedding for Generalized Zero-Shot Learning |
| abstract |
Abstract Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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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Abstract Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL recognition since it lacks semantic information, which is vital for recognizing the unseen classes. To tackle this issue, we propose to integrate the feature generation model with an embedding model. Our GZSL framework maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a semantic contrastive embedding (SCE) for our GZSL framework. Our SCE consists of attribute-level contrastive embedding and class-level contrastive embedding. They aim to obtain the transferable and discriminative information, respectively, in the embedding space. We evaluate our GZSL method with semantic contrastive embedding, named SCE-GZSL, on four benchmark datasets. The results show that our SCE-GZSL method can achieve the state-of-the-art or the second-best on these datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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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Semantic Contrastive Embedding for Generalized Zero-Shot Learning |
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Fu, Zhenyong Chen, Shuo Yang, Jian |
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10.1007/s11263-022-01656-y |
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2024-07-03T18:03:53.826Z |
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| score |
7.4019384 |