To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

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 …

Learning under a continuously changing data distribution with incorrect labels is a desirable
real-world problem yet challenging. Large body of continual learning (CL) methods …

Coreset is a small set that provides a data summary for a large dataset, such that training
solely on the small set achieves competitive performance compared with a large dataset. In …

The task of continual learning requires careful design of algorithms that can tackle
catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario …

Online continual learning suffers from an underfitted solution due to insufficient training for
prompt model updates (eg single-epoch training). To address the challenge we propose an …

Continual Learning (CL) has achieved rapid progress in recent years. However, it is still
largely unknown how to determine whether a CL model is trustworthy and how to foster its …

As a front-burner problem in incremental learning, class incremental semantic segmentation
(CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods …

In supervised continual learning, a deep neural network (DNN) is updated with an ever-
growing data stream. Unlike the offline setting where data is shuffled, we cannot make any …