Synthetic-to-Real Translation
55 papers with code • 4 benchmarks • 5 datasets
Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.
( Image credit: CYCADA )
Benchmarks
These leaderboards are used to track progress in Synthetic-to-Real Translation
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Libraries
Use these libraries to find Synthetic-to-Real Translation models and implementations
SYNTHIA
GTA5
Syn2Real
Sims4Action
WinSyn
Latest papers with no code
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i. e., segmentation and depth learning in an end-to-end manner.
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a source dataset with dense pixel-level annotations to an unlabeled target dataset.
Pixel-level Intra-domain Adaptation for Semantic Segmentation
Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks.
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer
To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain.
Cross-Region Domain Adaptation for Class-level Alignment
To cope with this, we propose a method that applies adversarial training to align two feature distributions in the target domain.
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation
To exploit the advantage of using multiple image translations, we propose an ensemble learning approach, where three classifiers calculate their prediction by taking as input features of different image translations, making each classifier learn independently, with the purpose of combining their outputs by sparse Multinomial Logistic Regression.
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation
To avoid the costly annotation of training data for unseen domains, unsupervised domain adaptation (UDA) attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain.
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works.
cGANs for Cartoon to Real-life Images
The image-to-image translation is a learning task to establish a visual mapping between an input and output image.
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching
Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias.
