Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing
In semantic segmentation, current deep convolutional neural networks rely heavily on extensive data to achieve superior segmentation results. However, these deep models have poor generalization ability across different domain datasets. To alleviate the degradation of the model’s performan...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10788698/ |
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