  Incremental semantic segmentation endeavors to segment newly encountered
classes while maintaining knowledge of old classes. However, existing methods
either 1) lack guidance from class-specific knowledge (i.e., old class
prototypes), leading to a bias towards new classes, or 2) constrain
class-shared knowledge (i.e., old model weights) excessively without
discrimination, resulting in a preference for old classes. In this paper, to
trade off model performance, we propose the Class-specific and Class-shared
Knowledge (Cs2K) guidance for incremental semantic segmentation. Specifically,
from the class-specific knowledge aspect, we design a prototype-guided pseudo
labeling that exploits feature proximity from prototypes to correct pseudo
labels, thereby overcoming catastrophic forgetting. Meanwhile, we develop a
prototype-guided class adaptation that aligns class distribution across
datasets via learning old augmented prototypes. Moreover, from the class-shared
knowledge aspect, we propose a weight-guided selective consolidation to
strengthen old memory while maintaining new memory by integrating old and new
model weights based on weight importance relative to old classes. Experiments
on public datasets demonstrate that our proposed Cs2K significantly improves
segmentation performance and is plug-and-play.
