Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Abstract The goal of Online Domain Adaptation for semantic segmentation is to handle
unforeseeable domain changes that occur during deployment, like sudden weather events …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

This paper proposes a new active learning method for semantic segmentation. The core of
our method lies in a new annotation query design. It samples informative local image …

Abstract Domain adaptation adapts models to various scenes with different appearances. In
this field active domain adaptation is crucial in effectively sampling a limited number of data …

For the task of semantic segmentation (SS) under domain shift, active learning (AL)
acquisition strategies based on image regions and pseudo labels are state-of-the-art (SoA) …

Object detection for autonomous haulage trucks in open-pit mines is challenging due to
unstructured environments. Collecting and annotating large-scale real-world images in …

Unsupervised domain adaption has been widely adopted in tasks with scarce annotated
data. Unfortunately, mapping the target-domain distribution to the source-domain …

Abstract Unsupervised Domain Adaptation (UDA) for semantic segmentation has been
widely studied to exploit the label-rich source data to assist the segmentation of unlabeled …

This work aims at active domain adaptation to transfer knowledge from a fully-labeled
source domain to an entirely unlabeled target domain. During the active learning period …