32 papers with code • 3 benchmarks • 4 datasets
Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.
( Image credit: CYCADA )
Domain adaptation is critical for success in new, unseen environments.
Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.
Ranked #13 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
Ranked #3 on Domain Adaptation on Synscapes-to-Cityscapes
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
Ranked #3 on Semantic Segmentation on GTAV-to-Cityscapes Labels
We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data.
Ranked #5 on Semantic Segmentation on GTAV-to-Cityscapes Labels
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
We consider the problem of unsupervised domain adaptation in semantic segmentation.
Ranked #18 on Synthetic-to-Real Translation on SYNTHIA-to-Cityscapes (MIoU (13 classes) metric)
In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation.
Ranked #7 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation.
Ranked #2 on Image-to-Image Translation on SYNTHIA-to-Cityscapes