7 datasets found

Few-shot segmentation aims to segment images containing objects from previously unseen classes using only a few annotated samples.

Few-shot segmentation aims to segment a target class using a small number of labeled images (support set). To extract information relevant to the target class, a dominant...

Universal medical image segmentation (UniverSeg) method for few-shot segmentation of medical images.

This paper proposes a framework to address the issue of requiring precise masks for existing FSS tasks, which address weakly-supervised few-shot segmentation tasks with only...

Few-shot segmentation aims at performing semantic segmentation on novel classes given a few annotated support samples.

Few-shot segmentation aims to segment novel classes by using the model trained on base classes.

PASCAL-5i and COCO-20i are datasets used for evaluation of few-shot segmentation.