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
Transferring to Real-World Layouts: A Depth-aware Framework for Scene AdaptationCode1
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene SegmentationCode0
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic SegmentationCode1
MIC: Masked Image Consistency for Context-Enhanced Domain AdaptationCode2
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDACode0
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic SegmentationCode1
Deliberated Domain Bridging for Domain Adaptive Semantic SegmentationCode1
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation—0
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic SegmentationCode1
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic SegmentationCode1
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