  Deep learning approaches are nowadays ubiquitously used to tackle computer
vision tasks such as semantic segmentation, requiring large datasets and
substantial computational power. Continual learning for semantic segmentation
(CSS) is an emerging trend that consists in updating an old model by
sequentially adding new classes. However, continual learning methods are
usually prone to catastrophic forgetting. This issue is further aggravated in
CSS where, at each step, old classes from previous iterations are collapsed
into the background. In this paper, we propose Local POD, a multi-scale pooling
distillation scheme that preserves long- and short-range spatial relationships
at feature level. Furthermore, we design an entropy-based pseudo-labelling of
the background w.r.t. classes predicted by the old model to deal with
background shift and avoid catastrophic forgetting of the old classes. Our
approach, called PLOP, significantly outperforms state-of-the-art methods in
existing CSS scenarios, as well as in newly proposed challenging benchmarks.
