The field of continual deep learning is an emerging field and a lot of
progress has been made. However, concurrently most of the approaches are only
tested on the task of image classification, which is not relevant in the field
of intelligent vehicles. Only recently approaches for class-incremental
semantic segmentation were proposed. However, all of those approaches are based
on some form of knowledge distillation. At the moment there are no
investigations on replay-based approaches that are commonly used for object
recognition in a continual setting. At the same time while unsupervised domain
adaption for semantic segmentation gained a lot of traction, investigations
regarding domain-incremental learning in an continual setting is not
well-studied. Therefore, the goal of our work is to evaluate and adapt
established solutions for continual object recognition to the task of semantic
segmentation and to provide baseline methods and evaluation protocols for the
task of continual semantic segmentation. We firstly introduce evaluation
protocols for the class- and domain-incremental segmentation and analyze
selected approaches. We show that the nature of the task of semantic
segmentation changes which methods are most effective in mitigating forgetting
compared to image classification. Especially, in class-incremental learning
knowledge distillation proves to be a vital tool, whereas in domain-incremental
learning replay methods are the most effective method