  Deep neural networks suffer from the major limitation of catastrophic
forgetting old tasks when learning new ones. In this paper we focus on class
incremental continual learning in semantic segmentation, where new categories
are made available over time while previous training data is not retained. The
proposed continual learning scheme shapes the latent space to reduce forgetting
whilst improving the recognition of novel classes. Our framework is driven by
three novel components which we also combine on top of existing techniques
effortlessly. First, prototypes matching enforces latent space consistency on
old classes, constraining the encoder to produce similar latent representation
for previously seen classes in the subsequent steps. Second, features
sparsification allows to make room in the latent space to accommodate novel
classes. Finally, contrastive learning is employed to cluster features
according to their semantics while tearing apart those of different classes.
Extensive evaluation on the Pascal VOC2012 and ADE20K datasets demonstrates the
effectiveness of our approach, significantly outperforming state-of-the-art
methods.
