Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks
Fully supervised deep neural networks for segmentation usually require a massive amount of pixel-level labels which are manually expensive to create. In this work, we develop a multi-task learning method to relax this constraint. We regard the segmentation problem as a sequence of approximation subp...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 05., Seite 3555-3567
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| 1. Verfasser: |
Ke, Rihuan
(VerfasserIn) |
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| Weitere Verfasser: |
Bugeau, Aurelie,
Papadakis, Nicolas,
Kirkland, Mark,
Schuetz, Peter,
Schonlieb, Carola-Bibiane |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2021
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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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