In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

In this work, we study the continual semantic segmentation problem, where the deep neural
networks are required to incorporate new classes continually without catastrophic forgetting …

Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic
forgetting to improve discrimination. Previous work mainly exploit regularization (eg …

Deep learning models are known to suffer from the problem of catastrophic forgetting when
they incrementally learn new classes. Continual learning for semantic segmentation (CSS) …

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 …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

Recent years have witnessed a great development of Convolutional Neural Networks in
semantic segmentation, where all classes of training images are simultaneously available …

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 …

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks
such as semantic segmentation, requiring large datasets and substantial computational …

Segmentation incremental learning (SIL) has received a lot of attention in recent years due
to the ability to overcome the problem of catastrophic forgetting. Our study found that …