Online class-incremental continual learning is a specific task of continual learning. It aims to
continuously learn new classes from data stream and the samples of data stream are seen …

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 …

Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …

Contemporary neural networks are limited in their ability to learn from evolving streams of
training data. When trained sequentially on new or evolving tasks, their accuracy drops …

Exemplar-free class-incremental learning is very challenging due to the negative effect of
catastrophic forgetting. A balance between stability and plasticity of the incremental process …

In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting
caused by the classifier's bias towards the current task has long posed a significant …

Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the
only challenge. This paper argues for another challenge in class-incremental learning (CIL) …

Abstract In Class-Incremental Learning (CIL) an image classification system is exposed to
new classes in each learning session and must be updated incrementally. Methods …

In this paper, we introduce audio-visual class-incremental learning, a class-incremental
learning scenario for audio-visual video recognition. We demonstrate that joint audio-visual …