Abstract Domain adaptive semantic segmentation aims to learn a model with the supervision
of source domain data, and produce satisfactory dense predictions on unlabeled target …

Unsupervised domain adaptation is critical to many real-world applications where label
information is unavailable in the target domain. In general, without further assumptions, the …

Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple
labeled source domains to an unlabeled target domain. Although current methods achieve …

The representative instance segmentation methods mostly segment different object
instances with a mask of the fixed resolution, eg, 28x 28 grid. However, a low-resolution …

Abstract Conventional Unsupervised Domain Adaptation (UDA) strives to minimize
distribution discrepancy between domains which neglects to harness rich semantics from …

Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich source
domain to a related but label-scarce target domain. Recently, increasing research has …

Most existing methods for unsupervised domain adaptation (UDA) rely on a shared network
to extract domain-invariant features. However, when facing multiple source domains …

Recent unsupervised domain adaptation methods have utilized vicinal space between the
source and target domains. However, the equilibrium collapse of labels, a problem where …

Abstract Multi-Source Domain Adaptation (MSDA) aims at training a classification model that
achieves small target error, by leveraging labeled data from multiple source domains and …

Weakly supervised instance segmentation using only bounding box annotations has
recently attracted much research attention. Most of the current efforts leverage low-level …