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Domain Adaptation On Cityscapes To Acdc

Metrics

mIoU

Results

Performance results of various models on this benchmark

Model Name
mIoU
Paper TitleRepository
DACS (DeepLabv2)41.2DACS: Domain Adaptation via Cross-domain Mixed Sampling-
Refign (DAFormer)65.5Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions-
DAFormer55.4DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation-
VBLC (DeepLabv2)47.8VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions-
MIC70.4MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation-
CMFormer60.1Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation-
Refign (HRDA)72.1Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions-
SoRA78.8SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning-
DANNet50.0DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation-
FDA (DeepLabv2)45.7FDA: Fourier Domain Adaptation for Semantic Segmentation-
VBLC (DAFormer)64.2VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions-
HRDA68.0HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation-
Rein77.6Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation-
MGCDA48.7Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation-
CoDA72.6CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning-
HALO71.9Hyperbolic Active Learning for Semantic Segmentation under Domain Shift-
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Domain Adaptation On Cityscapes To Acdc | SOTA | HyperAI