The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-
4) have sparked a wave of interest and research in the field of large language models …

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 …

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 …

Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the
generalization capability of most previous works could be fragile when countering hard …

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

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 …

Freezing the pre-trained backbone has become a standard paradigm to avoid overfitting in
few-shot segmentation. In this paper, we rethink the paradigm and explore a new …

Generalized few-shot semantic segmentation (GFSS) distinguishes pixels of base and novel
classes from the background simultaneously, conditioning on sufficient data of base classes …

Few-shot segmentation (FSS) aims to segment novel objects in a given query image with
only a few annotated support images. However, most previous best-performing methods …

Few-shot semantic segmentation is the task of learning to locate each pixel of the novel
class in the query image with only a few annotated support images. The current correlation …