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 …

As a special case of machine learning, incremental learning can acquire useful knowledge
from incoming data continuously while it does not need to access the original data. It is …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

Although deep neural networks enable impressive visual perception performance for
autonomous driving, their robustness to varying weather conditions still requires attention …

Deep models have shown to be vulnerable to catastrophic forgetting, a phenomenon that
the recognition performance on old data degrades when a pre-trained model is fine-tuned …

Continual learning is essential for real-world deployment when there is a need to quickly
adapt the model to new tasks without forgetting knowledge of old tasks. Existing work on …

Classical deep neural networks are limited in their ability to learn from emerging streams of
training data. When trained sequentially on new or evolving tasks, their performance …

The image distortions are complex and dynamically changing in the real-world scenario,
due to the fast development of the image processing system. The blind image quality …

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by
leveraging prior knowledge from abundant base data. Generalized few-shot object detection …