  The rise of deep neural networks has led to several breakthroughs for
semantic segmentation. In spite of this, a model trained on source domain often
fails to work properly in new challenging domains, that is directly concerned
with the generalization capability of the model. In this paper, we present a
novel memory-guided domain generalization method for semantic segmentation
based on meta-learning framework. Especially, our method abstracts the
conceptual knowledge of semantic classes into categorical memory which is
constant beyond the domains. Upon the meta-learning concept, we repeatedly
train memory-guided networks and simulate virtual test to 1) learn how to
memorize a domain-agnostic and distinct information of classes and 2) offer an
externally settled memory as a class-guidance to reduce the ambiguity of
representation in the test data of arbitrary unseen domain. To this end, we
also propose memory divergence and feature cohesion losses, which encourage to
learn memory reading and update processes for category-aware domain
generalization. Extensive experiments for semantic segmentation demonstrate the
superior generalization capability of our method over state-of-the-art works on
various benchmarks.
