Inherit with Distillation and Evolve with Contrast: Exploring Class
  Incremental Semantic Segmentation Without Exemplar Memory
Danpei Zhao Bo Yuan Zhenwei Shi
As a front-burner problem in incremental learning, class incremental semantic
segmentation (CISS) is plagued by catastrophic forgetting and semantic drift.
Although recent methods have utilized knowledge distillation to transfer
knowledge from the old model, they are still unable to avoid pixel confusion,
which results in severe misclassification after incremental steps due to the
lack of annotations for past and future classes. Meanwhile data-replay-based
approaches suffer from storage burdens and privacy concerns. In this paper, we
propose to address CISS without exemplar memory and resolve catastrophic
forgetting as well as semantic drift synchronously. We present Inherit with
Distillation and Evolve with Contrast (IDEC), which consists of a Dense
Knowledge Distillation on all Aspects (DADA) manner and an Asymmetric
Region-wise Contrastive Learning (ARCL) module. Driven by the devised dynamic
class-specific pseudo-labelling strategy, DADA distils intermediate-layer
features and output-logits collaboratively with more emphasis on
semantic-invariant knowledge inheritance. ARCL implements region-wise
contrastive learning in the latent space to resolve semantic drift among known
classes, current classes, and unknown classes. We demonstrate the effectiveness
of our method on multiple CISS tasks by state-of-the-art performance, including
Pascal VOC 2012, ADE20K and ISPRS datasets. Our method also shows superior
anti-forgetting ability, particularly in multi-step CISS tasks.