As a front-burner problem in incremental learning, class incremental semantic segmentation
(CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods …

The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation
task by incrementally learning newly added classes. Previous work has introduced …

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

Existing class-incremental semantic segmentation (CISS) methods mainly tackle
catastrophic forgetting and background shift, but often overlook another crucial issue. In …

Incremental semantic segmentation focuses on continually learning the segmentation of
new coming classes without obtaining the training data from previously seen classes …

Class-incremental semantic segmentation aims to incrementally learn new classes while
maintaining the capability to segment old ones, and suffers catastrophic forgetting since the …

Incremental learning of semantic segmentation has emerged as a promising strategy for
visual scene interpretation in the open-world setting. However, it remains challenging to …

Over the past years, semantic segmentation, similar to many other tasks in computer vision,
has benefited from the progress in deep neural networks, resulting in significantly improved …

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 …

Modern incremental learning for semantic segmentation methods usually learn new
categories based on dense annotations. Although achieve promising results, pixel-by-pixel …