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
Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation
luyvlei/BiSMAPs
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A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them.
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
lslrh/cpsl
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CVPR 2022
One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for training.
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
alpc91/transda
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After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation.
Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation
kaiizhang/mfa
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MFA basically considers three parallel information fusion strategies, i. e., the cross-model fusion, temporal fusion and a novel online-offline pseudo label fusion.
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation
Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation.
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
It improves the state of the art by 10. 8 mIoU for GTA-to-Cityscapes and 5. 4 mIoU for Synthia-to-Cityscapes and enables learning even difficult classes such as train, bus, and truck well.
SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning
binhuixie/spcl
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Although there is significant progress in supervised semantic segmentation, it remains challenging to deploy the segmentation models to unseen domains due to domain biases.
Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
qianyuzqy/RCCR
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In this paper, we propose a novel and fully end-to-end trainable approach, called regional contrastive consistency regularization (RCCR) for domain adaptive semantic segmentation.
Dual Path Learning for Domain Adaptation of Semantic Segmentation
In this paper, based on the observation that domain adaptation frameworks performed in the source and target domain are almost complementary in terms of image translation and SSL, we propose a novel dual path learning (DPL) framework to alleviate visual inconsistency.
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
qianyuzqy/CAMix
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The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels.
