Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …

Pre-trained vision-language models, eg CLIP, have been increasingly used to address the
challenging Open-Vocabulary Segmentation (OVS) task, benefiting from their well-aligned …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

Calibration-based methods have dominated RAW image denoising under extremely low-
light environments. However, these methods suffer from several main deficiencies: 1) the …

Image segmentation based on continual learning exhibits a critical drop of performance,
mainly due to catastrophic forgetting and background shift, as they are required to …

Incremental semantic segmentation focuses on continually learning the segmentation of
new coming classes without obtaining the training data from previously seen classes …

Abstract Class Incremental Semantic Segmentation (CISS), within Incremental Learning for
semantic segmentation, targets segmenting new categories while reducing the catastrophic …