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Semantic Segmentation On Dark Zurich

Metrics

mIoU

Results

Performance results of various models on this benchmark

Model Name	mIoU	Paper Title	Repository
HRDA	55.9	HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation	-
CIConv	34.5	Zero-Shot Day-Night Domain Adaptation with a Physics Prior	-
MIC	60.2	MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation	-
SePiCo (DeepLab v2 ResNet-101)	45.4	SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation	-
DANNet	36.76	DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation	-
DANNet (DeepLab v2 ResNet-101)	42.5	DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation	-
GCMA	42.0	Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation	-
MGCDA	42.5	Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation	-
Refign (HRDA)	63.9	Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions	-
Refign (DAFormer)	56.2	Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions	-
SePiCo	54.2	SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation	-
DAFormer	53.8	DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation	-
GPS-GLASS	46.4	GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data	-
CoDA	61.2	CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning	-

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Semantic Segmentation On Dark Zurich | SOTA | HyperAI