Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a
few labeled samples. Current FSS methods are commonly built on the assumption that their …

Few-shot semantic segmentation (FSS) has achieved great success on segmenting objects
of novel classes supported by only a few annotated samples. However existing FSS …

Few-shot segmentation performance declines substantially when facing images from a
domain different than the training domain effectively limiting real-world use cases. To …

Few-shot Segmentation aims to segment the interested objects in the query image with just
a handful of labeled samples (ie, support images). Previous schemes would leverage the …

Abstract Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of
segmenting novel categories from a distinct domain using only limited exemplars. In this …

Few-shot segmentation (FSS) aims to segment novel classes in a query image by using only
a small number of supporting images from base classes. However, in cross-domain few-shot …

Real-time segmentation plays an important role in numerous applications, including
autonomous driving and medical imaging, where accurate and instantaneous segmentation …

The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain
disparity between the training and inference phases, which can exist in either the input data …

Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data
learning in the medical image analysis scope. Despite the progress has been achieved …

The goal of few-shot semantic segmentation is to build a model using a small amount of
annotated data to generalize to a new object class. When there are significant differences …