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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
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic SegmentationCode2
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-TransferCode1
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic SegmentationCode1
Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic SegmentationCode1
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic SegmentationCode1
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic SegmentationCode1
Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation AdaptationCode1
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain AugmentationCode0
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic SegmentationCode1
SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive LearningCode1
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