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Semantic Segmentation On Gtav To Cityscapes 1

Metrics

mIoU

Results

Performance results of various models on this benchmark

Model Name
mIoU
Paper TitleRepository
HRDA + PiPa75.6PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
HRDA73.8HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
DAFormer + ProCST69.4ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
G2L59.7G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation-
ProDA+CRA58.6Cross-Region Domain Adaptation for Class-level Alignment-
MIC75.9MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
CCM49.9Content-Consistent Matching for Domain Adaptive Semantic Segmentation
SePiCo70.3SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation-
TransDA-B63.9Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
CMFormer55.3Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
DAFormer68.3DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
ProDA57.5Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
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Semantic Segmentation On Gtav To Cityscapes 1 | SOTA | HyperAI