Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Self-training is a competitive approach in domain adaptive segmentation, which trains the
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …

Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …

Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source
domain (eg synthetic data) to the target domain (eg real-world data) without requiring further …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

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 …

We propose an approach to domain adaptation for semantic segmentation that is both
practical and highly accurate. In contrast to previous work, we abandon the use of …

Semantic segmentation models based on convolutional neural networks have recently
displayed remarkable performance for a multitude of applications. However, these models …

The divergence between labeled training data and unlabeled testing data is a significant
challenge for recent deep learning models. Unsupervised domain adaptation (UDA) …

Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to
produce metric predictions. Even so, the resulting models will be geometry-specific, with …