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 …

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

Research on continual learning has recently led to a variety of work in unimodal community,
however little attention has been paid to multimodal tasks like visual question answering …

Continual Learning (CL) enables machine learning models to learn from continuously
shifting new training data in absence of data from old tasks. Recently pre-trained vision …

Deep Learning models have achieved remarkable performance in tasks such as image
classification or generation, often surpassing human accuracy. However, they can struggle …

Image segmentation based on continual learning exhibits a critical drop of performance,
mainly due to catastrophic forgetting and background shift, as they are required to …

Abstract Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things
and are becoming an integral part of our daily life. When tackling the evolving learning tasks …

Continual Learning (CL) has achieved rapid progress in recent years. However, it is still
largely unknown how to determine whether a CL model is trustworthy and how to foster its …

To enable machines to mimic human cognitive abilities and alleviate the catastrophic
forgetting problem in cross-modal image-text retrieval (CMITR), this paper proposes a novel …

Deep neural networks (DNNs) struggle to learn in dynamic settings because they mainly rely
on static datasets. Continual learning (CL) aims to overcome this limitation by enabling …