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 …

Replay is the reactivation of one or more neural patterns that are similar to the activation
patterns experienced during past waking experiences. Replay was first observed in …

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 …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

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 work, we study the continual semantic segmentation problem, where the deep neural
networks are required to incorporate new classes continually without catastrophic forgetting …

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 …

Attaining prototypical features to represent class distributions is well established in
representation learning. However, learning prototypes online from streaming data proves a …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Continual learning (CL) is widely regarded as crucial challenge for lifelong AI. However,
existing CL benchmarks, eg Permuted-MNIST and Split-CIFAR, make use of artificial …