Hierarchical few-shot learning with feature fusion driven by data and knowledge
Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Zhiping [verfasserIn] Zhao, Hong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Information sciences - New York, NY : Elsevier Science Inc., 1968, 639 |
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| Übergeordnetes Werk: |
volume:639 |
| DOI / URN: |
10.1016/j.ins.2023.119012 |
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| Katalog-ID: |
ELV009760385 |
|---|
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| 245 | 1 | 0 | |a Hierarchical few-shot learning with feature fusion driven by data and knowledge |
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| 520 | |a Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. | ||
| 650 | 4 | |a Few-shot learning | |
| 650 | 4 | |a Hierarchical classification | |
| 650 | 4 | |a Granular computing | |
| 650 | 4 | |a Feature fusion | |
| 650 | 4 | |a Data- and knowledge-driven | |
| 700 | 1 | |a Zhao, Hong |e verfasserin |0 (orcid)0000-0001-9339-1829 |4 aut | |
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10.1016/j.ins.2023.119012 doi (DE-627)ELV009760385 (ELSEVIER)S0020-0255(23)00597-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Wu, Zhiping verfasserin (orcid)0000-0003-3578-9024 aut Hierarchical few-shot learning with feature fusion driven by data and knowledge 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. Few-shot learning Hierarchical classification Granular computing Feature fusion Data- and knowledge-driven Zhao, Hong verfasserin (orcid)0000-0001-9339-1829 aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 639 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:639 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 639 |
| spelling |
10.1016/j.ins.2023.119012 doi (DE-627)ELV009760385 (ELSEVIER)S0020-0255(23)00597-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Wu, Zhiping verfasserin (orcid)0000-0003-3578-9024 aut Hierarchical few-shot learning with feature fusion driven by data and knowledge 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. Few-shot learning Hierarchical classification Granular computing Feature fusion Data- and knowledge-driven Zhao, Hong verfasserin (orcid)0000-0001-9339-1829 aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 639 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:639 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 639 |
| allfields_unstemmed |
10.1016/j.ins.2023.119012 doi (DE-627)ELV009760385 (ELSEVIER)S0020-0255(23)00597-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Wu, Zhiping verfasserin (orcid)0000-0003-3578-9024 aut Hierarchical few-shot learning with feature fusion driven by data and knowledge 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. Few-shot learning Hierarchical classification Granular computing Feature fusion Data- and knowledge-driven Zhao, Hong verfasserin (orcid)0000-0001-9339-1829 aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 639 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:639 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 639 |
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10.1016/j.ins.2023.119012 doi (DE-627)ELV009760385 (ELSEVIER)S0020-0255(23)00597-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Wu, Zhiping verfasserin (orcid)0000-0003-3578-9024 aut Hierarchical few-shot learning with feature fusion driven by data and knowledge 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. Few-shot learning Hierarchical classification Granular computing Feature fusion Data- and knowledge-driven Zhao, Hong verfasserin (orcid)0000-0001-9339-1829 aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 639 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:639 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 639 |
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10.1016/j.ins.2023.119012 doi (DE-627)ELV009760385 (ELSEVIER)S0020-0255(23)00597-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Wu, Zhiping verfasserin (orcid)0000-0003-3578-9024 aut Hierarchical few-shot learning with feature fusion driven by data and knowledge 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. Few-shot learning Hierarchical classification Granular computing Feature fusion Data- and knowledge-driven Zhao, Hong verfasserin (orcid)0000-0001-9339-1829 aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 639 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:639 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 639 |
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Wu, Zhiping ddc 070 fid LING bkl 54.00 bkl 53.71 misc Few-shot learning misc Hierarchical classification misc Granular computing misc Feature fusion misc Data- and knowledge-driven Hierarchical few-shot learning with feature fusion driven by data and knowledge |
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070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Hierarchical few-shot learning with feature fusion driven by data and knowledge Few-shot learning Hierarchical classification Granular computing Feature fusion Data- and knowledge-driven |
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hierarchical few-shot learning with feature fusion driven by data and knowledge |
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Hierarchical few-shot learning with feature fusion driven by data and knowledge |
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Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. |
| abstractGer |
Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. |
| abstract_unstemmed |
Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance. |
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| score |
7.40026 |