SOTAVerified

Synthetic-to-Real Translation

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

( Image credit: CYCADA )

Papers

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Showing 51–68 of 68 papers

TitleStatusHype
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent LabelingCode0
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain AdaptationCode0
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDACode0
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain AugmentationCode0
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic SegmentationCode0
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial ApproachCode0
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene SegmentationCode0
Curriculum Domain Adaptation for Semantic Segmentation of Urban ScenesCode0
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation—0
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer—0
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization—0
cGANs for Cartoon to Real-life Images—0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—0
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes—0
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement—0
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation—0
Cross-Region Domain Adaptation for Class-level Alignment—0
Pixel-level Intra-domain Adaptation for Semantic Segmentation—0
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