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Domain Adaptation On Cityscapes To Acdc

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mIoU

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
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
SoRA78.8SoRA: Singular Value Decomposed Low-Rank 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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