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Models/Uncertainty

Uncertainty

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology3 results

Domain AdaptationonGTAV-to-Cityscapes Labels
mIoU· 2020-03-08
50.3
best: 75.9 (MIC)
SOTA
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation arXiv:2003.03773
Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· 2020-03-08
47.9
best: 78.1 (HALO)
SOTA
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation arXiv:2003.03773
Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· 2020-03-08
54.9
best: 75.9 (DCF)
SOTA
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation arXiv:2003.03773

Other3 results

Unsupervised Domain AdaptationonGTAV-to-Cityscapes Labels
mIoU· 2020-03-08
50.3
best: 75.9 (MIC)
SOTA
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation arXiv:2003.03773
Unsupervised Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· 2020-03-08
47.9
best: 69.3 (DCF)
SOTA
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation arXiv:2003.03773
Unsupervised Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· 2020-03-08
54.9
best: 75.9 (DCF)
SOTA
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation arXiv:2003.03773