In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

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 …

Despite the recent advances in multi-task learning of dense prediction problems, most
methods rely on expensive labelled datasets. In this paper, we present a label efficient …

Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine
learning models trained on a source domain to perform well on unlabeled or even unseen …

With autonomous industries on the rise, domain adaptation of the visual perception stack is
an important research direction due to the cost savings promise. Much prior art was …

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts
between source and target domains, by applying a well-performed model in an unlabeled …

The recent enthusiasm for open-world vision systems show the high interest of the
community to perform perception tasks outside of the closed-vocabulary benchmark setups …

In semi-supervised semantic segmentation, a model is trained with a limited number of
labeled images along with a large corpus of unlabeled images to reduce the high annotation …

We present a novel approach for metric dense depth estimation based on the fusion of a
single-view image and a sparse, noisy Radar point cloud. The direct fusion of …