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 …

We propose a simple but effective source-free domain adaptation (SFDA) method. Treating
SFDA as an unsupervised clustering problem and following the intuition that local neighbors …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

Machine learning systems deployed in the wild are often trained on a source distribution but
deployed on a different target distribution. Unlabeled data can be a powerful point of …

Deep models must learn robust and transferable representations in order to perform well on
new domains. While domain transfer methods (eg, domain adaptation, domain …

Despite the emergence of principled methods for domain adaptation under label shift, their
sensitivity to shifts in class conditional distributions is precariously under explored …

Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge
of the source domain to the target one to improve the adaptability of the segmentation model …

Few-shot semantic segmentation intends to predict pixel level categories using only a few
labeled samples. Existing few-shot methods focus primarily on the categories sampled from …

Unsupervised person re-identification is a challenging and promising task in computer
vision. Nowadays unsupervised person re-identification methods have achieved great …