SOTAVerified

Synthetic-to-Real Translation

Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.

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Papers

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Showing 1–10 of 68 papers

TitleStatusHype
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic SegmentationCode2
MIC: Masked Image Consistency for Context-Enhanced Domain AdaptationCode2
Confidence Regularized Self-TrainingCode1
Bidirectional Learning for Domain Adaptation of Semantic SegmentationCode1
Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic SegmentationCode1
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic SegmentationCode1
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic SegmentationCode1
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene SegmentationCode1
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic SegmentationCode1
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban ScenesCode1
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