Rethinking Generalized Zero-Shot Learning : A Synthesized Per-Instance Attribute Perspective
Generalized zero-shot learning (GZSL) shows great potential for improving generalization to unseen classes in real-world scenarios. However, most GZSL methods depend on benchmark datasets with per-class attribute annotations, which creates a large semantic gap and worsens the domain shift problem in...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 34(2025) vom: 12., Seite 5847-5859
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| 1. Verfasser: |
Tang, Chenwei
(VerfasserIn) |
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| Weitere Verfasser: |
Wang, Ying,
Xie, Wei,
Zhang, Qianjun,
Xiao, Rong,
He, Zhenan,
Lv, Jiancheng |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2025
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article |
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