Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Semantic segmentation plays a fundamental role in a broad variety of computer vision
applications, providing key information for the global understanding of an image. Yet, the …

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 …

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 …

Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …

Abstract Domain generalization aims to learn a generalizable model from aknown'source
domain for variousunknown'target domains. It has been studied widely by domain …

Unsupervised domain adaptation without accessing expensive annotation processes of
target data has achieved remarkable successes in semantic segmentation. However, most …

Abstract Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the
labeled source domain to an unlabeled target domain. In this paper, we present Prototypical …

Instance contrast for unsupervised representation learning has achieved great success in
recent years. In this work, we explore the idea of instance contrastive learning in …

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring
semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable …