Semi-Supervised Learning for FGVC With Out-of-Category Data
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for. Semi-supervised learning (SSL) techniques, acquiring knowledge from unlabeled data, provide a considerable means...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 5 vom: 04. Apr., Seite 2658-2671
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| 1. Verfasser: |
Du, Ruoyi
(VerfasserIn) |
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| Weitere Verfasser: |
Chang, Dongliang,
Ma, Zhanyu,
Liang, Kongming,
Song, Yi-Zhe,
Guo, Jun |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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