The domain adaptation of satellite images has recently gained increasing attention to
overcome the limited generalization abilities of machine learning models when segmenting …

We propose a new problem formulation and a corresponding evaluation framework to
advance research on unsupervised domain adaptation for semantic image segmentation …

While ground truth depth data remains hard to obtain, self-supervised monocular depth
estimation methods enjoy growing attention. Much research in this area aims at improving …

This paper deals with the challenging online monocular depth adaptation task that aims to
train an initial depth estimation model in a source domain and continuously adapt the model …

In recent years, there has been significant progress in overcoming the negative effects of
domain shift in semantic segmentation. Yet, existing unsupervised domain adaptation …

Surgical instrument segmentation in robot-assisted surgery (RAS)-especially that using
learning-based models-relies on the assumption that training and testing videos are …

In the academic field the research on human motion prediction tasks mainly focuses on
exploiting the observed information to forecast human movements accurately in the near …

RGB-D scene understanding under monocular camera is an emerging and challenging
topic with many potential applications. In this paper, we propose a novel Task-Recursive …

The scarcity of annotated surgical data in robot-assisted surgery (RAS) motivates prior works
to borrow related domain knowledge to achieve promising segmentation results in surgical …

Online stereo adaptation tackles the domain shift problem, caused by different environments
between synthetic (training) and real (test) datasets, to promptly adapt stereo models in …