Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

LiDAR-based 3D point cloud recognition has benefited various applications. Without
specially considering the LiDAR point distribution, most current methods suffer from …

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 …

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

Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks
when learning new ones. In this paper we focus on class incremental continual learning in …

We introduce the integrative task of few-shot classification and segmentation (FS-CS) that
aims to both classify and segment target objects in a query image when the target classes …

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

The booming of 3D recognition in the 2020s began with the introduction of point cloud
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …

Existing semantic segmentation methods improve generalization capability, by regularizing
various images to a canonical feature space. While this process contributes to …

Abstract Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic
Segmentation (FSS) to simultaneously segment unseen classes and seen classes during …