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 …

Few-shot class incremental learning (FSCIL) aims to address catastrophic forgetting during
class incremental learning in a few-shot learning setting. In this paper, we approach the …

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

Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …

In autonomous driving, even a meticulously trained model can encounter failures when
facing unfamiliar scenarios. One of these scenarios can be formulated as an online …

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 …

Incremental Learning (IL) aims to develop Machine Learning (ML) models that can learn
from continuous streams of data and mitigate catastrophic forgetting. We analyse the current …

As there exist competitive subnetworks within a dense network in concert with Lottery Ticket
Hypothesis, we introduce a novel neuron-wise task incremental learning method, namely …

Ultra-high-definition (UHD) technology has attracted widespread attention due to its
exceptional visual quality, but it also poses new challenges for low-light image …