Continual learning (CL) is under-explored in the video domain. The few existing works
contain splits with imbalanced class distributions over the tasks, or study the problem in …

Class-incremental learning is one of the most important settings for the study of Continual
Learning, as it closely resembles real-world application scenarios. With constrained memory …

Modern machine learning pipelines are limited due to data availability, storage quotas,
privacy regulations, and expensive annotation processes. These constraints make it difficult …

Catastrophic forgetting is one of the most critical challenges in Continual Learning (CL).
Recent approaches tackle this problem by projecting the gradient update orthogonal to the …

Online class-incremental continual learning aims to learn new classes continually from a
never-ending and single-pass data stream, while not forgetting the learned knowledge of old …

In Continual Learning, a Neural Network is trained on a stream of data whose distribution
shifts over time. Under these assumptions, it is especially challenging to improve on classes …

Online class-incremental continual learning (CL) studies the problem of learning new
classes continually from an online non-stationary data stream, intending to adapt to new …

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 …

Continual learning is a branch of deep learning that seeks to strike a balance between
learning stability and plasticity. The CVPR 2020 CLVision Continual Learning for Computer …

Online Continual Learning (OCL), as a core step towards achieving human-level
intelligence, aims to incrementally learn and accumulate novel concepts from streaming …