Semantic Segmentation On Gtav To Cityscapes 1

Métriques

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
MIC	75.9	MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
HRDA + PiPa	75.6	PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
HRDA	73.8	HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
SePiCo	70.3	SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
DAFormer + ProCST	69.4	ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
DAFormer	68.3	DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
TransDA-B	63.9	Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
G2L	59.7	G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation
ProDA+CRA	58.6	Cross-Region Domain Adaptation for Class-level Alignment
ProDA	57.5	Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
CMFormer	55.3	Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
CCM	49.9	Content-Consistent Matching for Domain Adaptive Semantic Segmentation

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Semantic Segmentation On Gtav To Cityscapes 1 | SOTA | HyperAI