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 …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

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 …

State-of-the-art deep neural networks are still struggling to address the catastrophic
forgetting problem in continual learning. In this paper, we propose one simple paradigm …

We address the problem of class incremental learning, which is a core step towards
achieving adaptive vision intelligence. In particular, we consider the task setting of …

Open-vocabulary models like CLIP achieve high accuracy across many image classification
tasks. However, there are still settings where their zero-shot performance is far from optimal …

For future learning systems, incremental learning is desirable because it allows for: efficient
resource usage by eliminating the need to retrain from scratch at the arrival of new data; …

This study proposes a novel unified and unsupervised end-to-end image fusion network,
termed as U2Fusion, which is capable of solving different fusion problems, including multi …

Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …

Continual learning is a realistic learning scenario for AI models. Prevalent scenario of
continual learning, however, assumes disjoint sets of classes as tasks and is less realistic …