Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, ie,
model performance deterioration on past tasks when learning a new task. However, the …

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 …

Continual learning methods aim at training a neural network from sequential data with
streaming labels, relieving catastrophic forgetting. However, existing methods are based on …

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

Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …

Continual learning can empower vision-language models to continuously acquire new
knowledge without the need for access to the entire historical dataset. However mitigating …

A primary goal of class-incremental learning is to strike a balance between stability and
plasticity, where models should be both stable enough to retain knowledge learned from …

Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic
forgetting to improve discrimination. Previous work mainly exploit regularization (eg …

Class-incremental learning (CIL) has been widely studied under the setting of starting from a
small number of classes (base classes). Instead, we explore an understudied real-world …

Real-world scenarios are usually accompanied by continuously appearing classes with
scare labeled samples, which require the machine learning model to incrementally learn …