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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2022.3191696
|2 doi
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2 |
|a pubmed24n1145.xml
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|a (DE-627)NLM343698722
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|a (NLM)35849673
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Pourpanah, Farhad
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A Review of Generalized Zero-Shot Learning Methods
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 10.04.2023
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|a Date Revised 10.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. First, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations
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|a Journal Article
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| 700 |
1 |
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|a Abdar, Moloud
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Luo, Yuxuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Xinlei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Ran
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lim, Chee Peng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Xi-Zhao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Q M Jonathan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 4 vom: 18. Apr., Seite 4051-4070
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:4
|g day:18
|g month:04
|g pages:4051-4070
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2022.3191696
|3 Volltext
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