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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 41–50 of 68 papers

TitleStatusHype
DACS: Domain Adaptation via Cross-domain Mixed SamplingCode1
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer—0
Cross-Region Domain Adaptation for Class-level Alignment—0
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization—0
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes—0
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation—0
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation—0
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement—0
Pixel-level Intra-domain Adaptation for Semantic Segmentation—0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—0
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