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
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
BiQiWHU/CMFormer
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Unlike domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes in urban landscapes, weather conditions, lighting, and other factors.
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
lhoyer/mic
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CVPR 2023
MIC significantly improves the state-of-the-art performance across the different recognition tasks for synthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA.
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
TingHLiao/ELDA
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Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
chen742/PiPa
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In an attempt to fill this gap, we propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation that facilitates intra-image pixel-wise correlations and patch-wise semantic consistency against different contexts.
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
xiaoachen98/DDB
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In this work, we resort to data mixing to establish a deliberated domain bridging (DDB) for DASS, through which the joint distributions of source and target domains are aligned and interacted with each in the intermediate space.
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
user0407/CLUDA
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In this work, we propose CLUDA, a simple, yet novel method for performing unsupervised domain adaptation (UDA) for semantic segmentation by incorporating contrastive losses into a student-teacher learning paradigm, that makes use of pseudo-labels generated from the target domain by the teacher network.
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation
ljjcoder/ehtdi
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Such a strategy can generate the object boundaries in target domain (edge of target-domain object areas) with the correct labels.
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
lhoyer/hrda
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Therefore, we propose HRDA, a multi-resolution training approach for UDA, that combines the strengths of small high-resolution crops to preserve fine segmentation details and large low-resolution crops to capture long-range context dependencies with a learned scale attention, while maintaining a manageable GPU memory footprint.
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
This new data has a reduced domain gap from the desired target domain, which facilitates the applied UDA approach to close the gap further.
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
bit-da/sepico
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Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain.
