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 11–20 of 68 papers

TitleStatusHype
Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic SegmentationCode1
Confidence Regularized Self-TrainingCode1
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic SegmentationCode1
Content-Consistent Matching for Domain Adaptive Semantic SegmentationCode1
Context-Aware Mixup for Domain Adaptive Semantic SegmentationCode1
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban ScenesCode1
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic SegmentationCode1
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic SegmentationCode1
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic SegmentationCode1
Domain Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
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