Deep learning has revolutionized the field of artificial intelligence. Based on the statistical
correlations uncovered by deep learning-based methods, computer vision tasks, such as …

The recent large-scale pre-trained models like CLIP have aroused great concern in vision-
language tasks. However, when required to match image-text data collected in a streaming …

Existing class incremental learning methods typically employ knowledge distillation to
minimize discrepancies in model outputs. However, these methods are restricted by the …

Computer vision systems have witnessed rapid progress over the past two decades due to
multiple advances in the field. As these systems are increasingly being deployed in high …

Continual learning aims to acquire new knowledge while retaining past information. Class-
incremental learning (CIL) presents a challenging scenario where classes are introduced …

Residual connections are one of the most important components in neural network
architectures for mitigating the vanishing gradient problem and facilitating the training of …

Deep learning heavily relies on statistical correlations to drive artificial intelligence (AI)
innovations, particularly in computer vision applications like autonomous driving and …

To handle real-world complexities, intelligent systems need to incrementally acquire,
update, and use knowledge throughout their lifetime, a capability known as continual …