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 …

Video segmentation—partitioning video frames into multiple segments or objects—plays a
critical role in a broad range of practical applications, from enhancing visual effects in movie …

Humans can orient themselves in their 3D environments using simple 2D maps. Differently,
algorithms for visual localization mostly rely on complex 3D point clouds that are expensive …

Abstract Domain adaptation for semantic segmentation aims to improve the model
performance in the presence of a distribution shift between source and target domain …

Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their
mutual relationships. By using shared resources to simultaneously calculate multiple …

The semi-supervised semantic segmentation methods utilize the unlabeled data to increase
the feature discriminative ability to alleviate the burden of the annotated data. However, the …

Learning discriminative task-specific features simultaneously for multiple distinct tasks is a
fundamental problem in multi-task learning. Recent state-of-the-art models consider directly …

Recently, point cloud domain adaptation (DA) practices have been implemented to improve
the generalization ability of deep learning models on point cloud data. However, variations …