State-of-the-art semantic segmentation methods require sufficient labeled data to achieve
good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation …

Conventional deep CNN-based segmentation approaches have achieved satisfactory
performance in recent years, however, they are essentially Big Data-driven technologies …

Over the past few years, we have witnessed the success of deep learning in image
recognition thanks to the availability of large-scale human-annotated datasets such as …

Abstract Few-shot segmentation~(FSS) aims at performing semantic segmentation on novel
classes given a few annotated support samples. With a rethink of recent advances, we find …

Recently few-shot segmentation (FSS) has been extensively developed. Most previous
works strive to achieve generalization through the meta-learning framework derived from …

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated
samples. Existing methods suffer the problem of feature undermining, ie potential novel …

Few-shot segmentation aims to segment unseen-class objects given only a handful of
densely labeled samples. Prototype learning, where the support feature yields a singleor …

The key to the success of few-shot segmentation (FSS) lies in how to effectively utilize
support samples. Most solutions compress support foreground (FG) features into prototypes …

Existing few-shot segmentation methods are based on the meta-learning strategy and
extract instance knowledge from a support set and then apply the knowledge to segment …

The deep CNNs in image semantic segmentation typically require a large number of
densely-annotated images for training and have difficulties in generalizing to unseen object …