  Class Incremental Semantic Segmentation (CISS) has been a trend recently due
to its great significance in real-world applications. Although the existing
CISS methods demonstrate remarkable performance, they either leverage the
high-level knowledge (feature) only while neglecting the rich and diverse
knowledge in the low-level features, leading to poor old knowledge preservation
and weak new knowledge exploration; or use multi-level features for knowledge
distillation by retraining a heavy backbone, which is computationally
intensive. In this paper, we for the first time investigate the efficient
multi-grained knowledge reuse for CISS, and propose a novel method, Evolving
kNowleDge minING (ENDING), employing a frozen backbone. ENDING incorporates two
key modules: evolving fusion and semantic enhancement, for dynamic and
comprehensive exploration of multi-grained knowledge. Evolving fusion is
tailored to extract knowledge from individual low-level feature using a
personalized lightweight network, which is generated from a meta-net, taking
the high-level feature as input. This design enables the evolution of knowledge
mining and fusing when applied to incremental new classes. In contrast,
semantic enhancement is specifically crafted to aggregate prototype-based
semantics from multi-level features, contributing to an enhanced
representation. We evaluate our method on two widely used benchmarks and
consistently demonstrate new state-of-the-art performance. The code is
available at https://github.com/zhiheLu/ENDING_ISS.
