Visual semantic segmentation aims at separating a visual sample into diverse blocks with
specific semantic attributes and identifying the category for each block, and it plays a crucial …

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

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

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 …

Few-shot semantic segmentation (FSS) is an important task for novel (unseen) object
segmentation under the data-scarcity scenario. However, most FSS methods rely on …

Prompt learning is an efficient approach to adapt transformers by inserting learnable set of
parameters into the input and intermediate representations of a pre-trained model. In this …

Despite impressive developments in deep convolutional neural networks for medical
imaging, the paradigm of supervised learning requires numerous annotations in training to …

Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few
annotated samples. Most current FSS methods follow the paradigm of mining the semantics …