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 this paper we describe the design and the ideas motivating a new Continual Learning
benchmark for Autonomous Driving (CLAD), that focuses on the problems of object …

The ability to learn new concepts continually is necessary in this ever-changing world.
However, deep neural networks suffer from catastrophic forgetting when learning new …

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 …

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 …

Computer vision models suffer from a phenomenon known as catastrophic forgetting when
learning novel concepts from continuously shifting training data. Typical solutions for this …

Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

Continual learning (CL) can help pre-trained vision-language models efficiently adapt to
new or under-trained data distributions without re-training. Nevertheless, during the …

Recent work on large language models relies on the intuition that most natural language
processing tasks can be described via natural language instructions. Language models …

Class-incremental learning (CIL) has achieved remarkable successes in learning new
classes consecutively while overcoming catastrophic forgetting on old categories. However …