Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …

Continual learning requires the model to learn multiple tasks in a sequential order. To
perform continual learning, the model must possess the abilities to maintain performance on …

Continual learning empowers models to adapt autonomously to the ever-changing
environment or data streams without forgetting old knowledge. Prompt-based approaches …

Precipitation nowcasting plays an important role in our life. Many deep learning-based
methods are proposed for precipitation nowcasting by predicting radar echo sequence over …

In this paper we focus on a challenging Online Task-Free Class Incremental Learning
(OTFCIL) problem. Different from the existing methods that continuously learn the feature …

We present a novel semantic segmentation approach for incremental nuclei segmentation
from histopathological images which is a very challenging task as we have to incrementally …

Online continual learning aims to continuously train neural networks from a continuous data
stream with a single pass-through data. As the most effective approach, the rehearsal-based …

Food image classification is essential for monitoring health and tracking dietary in image-
based dietary assessment methods. However, conventional systems often rely on static …

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of
models to rapidly learn new information in real-world scenarios where data follows long …

This paper investigates a new, practical, but challenging problem named Non-exemplar
Online Class-incremental continual Learning (NO-CL), which aims to preserve the …