Domain Adaptation On Cityscapes To Acdc

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mIoU

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
SoRA	78.8	SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning
Rein	77.6	Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
CoDA	72.6	CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
Refign (HRDA)	72.1	Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
HALO	71.9	Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
MIC	70.4	MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
HRDA	68.0	HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
Refign (DAFormer)	65.5	Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
VBLC (DAFormer)	64.2	VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions
CMFormer	60.1	Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
DAFormer	55.4	DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
DANNet	50.0	DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
MGCDA	48.7	Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
VBLC (DeepLabv2)	47.8	VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions
FDA (DeepLabv2)	45.7	FDA: Fourier Domain Adaptation for Semantic Segmentation
DACS (DeepLabv2)	41.2	DACS: Domain Adaptation via Cross-domain Mixed Sampling

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Domain Adaptation On Cityscapes To Acdc | SOTA | HyperAI