The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting
unseen classes with only a handful of annotations. Previous methods limited to the semantic …

Prototype learning is extensively used for few-shot segmentation. Typically, a single
prototype is obtained from the support feature by averaging the global object information …

Existing few-shot segmentation methods have achieved great progress based on the
support-query matching framework. But they still heavily suffer from the limited coverage of …

Few-shot semantic segmentation aims at learning to segment a target object from a query
image using only a few annotated support images of the target class. This challenging task …

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 semantic segmentation (FSS) aims to segment unseen class objects given very
few densely-annotated support images from the same class. Existing FSS methods find the …

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 …

Currently, the state-of-the-art methods treat few-shot semantic segmentation task as a
conditional foreground-background segmentation problem, assuming each class is …

Semantic segmentation assigns category labels to each pixel in an image, enabling
breakthroughs in fields such as autonomous driving and robotics. Deep Neural Networks …