  A fundamental and challenging problem in deep learning is catastrophic
forgetting, i.e. the tendency of neural networks to fail to preserve the
knowledge acquired from old tasks when learning new tasks. This problem has
been widely investigated in the research community and several Incremental
Learning (IL) approaches have been proposed in the past years. While earlier
works in computer vision have mostly focused on image classification and object
detection, more recently some IL approaches for semantic segmentation have been
introduced. These previous works showed that, despite its simplicity, knowledge
distillation can be effectively employed to alleviate catastrophic forgetting.
In this paper, we follow this research direction and, inspired by recent
literature on contrastive learning, we propose a novel distillation framework,
Uncertainty-aware Contrastive Distillation (\method). In a nutshell, \method~is
operated by introducing a novel distillation loss that takes into account all
the images in a mini-batch, enforcing similarity between features associated to
all the pixels from the same classes, and pulling apart those corresponding to
pixels from different classes. In order to mitigate catastrophic forgetting, we
contrast features of the new model with features extracted by a frozen model
learned at the previous incremental step. Our experimental results demonstrate
the advantage of the proposed distillation technique, which can be used in
synergy with previous IL approaches, and leads to state-of-art performance on
three commonly adopted benchmarks for incremental semantic segmentation. The
code is available at \url{https://github.com/ygjwd12345/UCD}.
