  Continual Learning is a step towards lifelong intelligence where models
continuously learn from recently collected data without forgetting previous
knowledge. Existing continual learning approaches mostly focus on image
classification in the class-incremental setup with clear task boundaries and
unlimited computational budget. This work explores the problem of Online
Domain-Incremental Continual Segmentation (ODICS), where the model is
continually trained over batches of densely labeled images from different
domains, with limited computation and no information about the task boundaries.
ODICS arises in many practical applications. In autonomous driving, this may
correspond to the realistic scenario of training a segmentation model over time
on a sequence of cities. We analyze several existing continual learning methods
and show that they perform poorly in this setting despite working well in
class-incremental segmentation. We propose SimCS, a parameter-free method
complementary to existing ones that uses simulated data to regularize continual
learning. Experiments show that SimCS provides consistent improvements when
combined with different CL methods.
