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 …

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 …

In this paper, we introduce audio-visual class-incremental learning, a class-incremental
learning scenario for audio-visual video recognition. We demonstrate that joint audio-visual …

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 …

Recent advances in generative models for language have enabled the creation of
convincing synthetic text or deepfake text. Prior work has demonstrated the potential for …

Continual learning in task-oriented dialogue systems can allow us to add new domains and
functionalities through time without incurring the high cost of a whole system retraining. In …

Large Transformer models have achieved impressive performance in many natural
language tasks. In particular, Transformer based language models have been shown to …

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

A desirable dialog system should be able to continually learn new skills without forgetting
old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually …

Traditional learning systems are trained in closed-world for a fixed number of classes, and
need pre-collected datasets in advance. However, new classes often emerge in real-world …