Depth Integrated Multi-Task Prototypical Learning With Self Refinement for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) serves as a potential alternative for improving cross- domain segmentation tasks. Recent UDA approaches have identified class-wise prototypes and leveraged them to guide the segmentation process in the target domain. However, these methods overlook additional inf...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11007591/ |
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