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 …

Incremental object detection (IOD) aims to train an object detector in phases, each with
annotations for new object categories. As other incremental settings, IOD is subject to …

Class-incremental learning (CIL) has achieved remarkable successes in learning new
classes consecutively while overcoming catastrophic forgetting on old categories. However …

A primary goal of class-incremental learning is to strike a balance between stability and
plasticity, where models should be both stable enough to retain knowledge learned from …

Class-incremental learning (CIL) aims to train a classification model while the number of
classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity …

Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …

This paper focuses on the prevalent stage interference and stage performance imbalance of
incremental learning. To avoid obvious stage learning bottlenecks, we propose a new …

In class-incremental learning, the model is expected to learn new classes continually while
maintaining knowledge on previous classes. The challenge here lies in preserving the …

Class-incremental semantic segmentation aims to incrementally learn new classes while
maintaining the capability to segment old ones, and suffers catastrophic forgetting since the …

Modern artificial intelligence systems require class-incremental learning while suffering from
catastrophic forgetting in many real-world applications. Due to the missing knowledge of …