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 …

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 …

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 …

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

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 …

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

The problem of class incremental learning (CIL) is considered. State-of-the-art approaches
use a dynamic architecture based on network expansion (NE), in which a task expert is …

Incremental learning aims to overcome catastrophic forgetting when learning deep networks
from sequential tasks. With impressive learning efficiency and performance, prompt-based …

Online continual learning (CL) aims to learn new knowledge and consolidate previously
learned knowledge from non-stationary data streams. Due to the time-varying training …

机器学习技术成功地应用于计算机视觉, 自然语言处理和语音识别等众多领域. 然而,
现有的大多数机器学习模型在部署后类别和参数是固定的, 只能泛化到训练集中出现的类别 …