In this work, we address the challenging task of few-shot segmentation. Previous few-shot
segmentation methods mainly employ the information of support images as guidance for …

We tackle the challenging task of few-shot segmentation in this work. It is essential for few-
shot semantic segmentation to fully utilize the support information. Previous methods …

Few-shot segmentation aims at assigning a category label to each image pixel with few
annotated samples. It is a challenging task since the dense prediction can only be achieved …

Few-shot segmentation has been attracting a lot of attention due to its effectiveness to
segment unseen object classes with a few annotated samples. Most existing approaches …

We show that the way inference is performed in few-shot segmentation tasks has a
substantial effect on performances--an aspect often overlooked in the literature in favor of …

Few-shot segmentation is a challenging dense prediction task, which entails segmenting a
novel query image given only a small annotated support set. The key problem is thus to …

Few-shot segmentation aims to learn a model that can quickly adapt to new classes with
limited labeled images. It remains challenging due to the large discrepancy of the targets …

Existing few-shot segmentation methods usually extract foreground prototypes from support
images to guide query image segmentation. However different background contexts of …

Mainstream few-shot segmentation methods meet performance bottleneck due to the data
scarcity of novel classes with insufficient intra-class variations, which results in a biased …

Few-shot segmentation (FSS) expects models trained on base classes to work on novel
classes with the help of a few support images. However, when there exists a domain gap …