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 …

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

Real-time learning concerns the ability of learning systems to acquire knowledge over time,
enabling their adaptation and generalization to novel tasks. It is a critical ability for …

Currently, most applications in the Industrial Internet of Things (IIoT) still rely on CNN-based
neural networks. Although Transformer-based large models (LMs), including language …

Exemplar-free class-incremental learning using a backbone trained from scratch and
starting from a small first task presents a significant challenge for continual representation …

Multimodal foundation models serve numerous applications at the intersection of vision and
language. Still, despite being pretrained on extensive data, they become outdated over time …

Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from
very few data (5 samples) without forgetting the previously learned classes. Recent works in …

Continual learning methods based on pre-trained models (PTM) have recently gained
attention which adapt to successive downstream tasks without catastrophic forgetting. These …

Continual learning requires a model to adapt to ongoing changes in the data distribution,
and often to the set of tasks to be performed. It is rare, however, that the data and task …