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 …

Federated learning-based semantic segmentation (FSS) has drawn widespread attention
via decentralized training on local clients. However, most FSS models assume categories …

Recently few-shot object detection (FSOD) in remote sensing images (RSIs) has drawn
increasing attention. However, the current FSOD methods in RSIs merely focus on the …

Abstract Continual Semantic Segmentation (CSS) extends static semantic segmentation by
incrementally introducing new classes for training. To alleviate the catastrophic forgetting …

Class incremental semantic segmentation aims to strike a balance between the model's
stability and plasticity by maintaining old knowledge while adapting to new concepts …

Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …

Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic
forgetting to improve discrimination. Previous work mainly exploit regularization (eg …

In incremental learning, replaying stored samples from previous tasks together with current
task samples is one of the most efficient approaches to address catastrophic forgetting …

In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting
caused by the classifier's bias towards the current task has long posed a significant …