  Semantic segmentation in a supervised learning manner has achieved
significant progress in recent years. However, its performance usually drops
dramatically due to the data-distribution discrepancy between seen and unseen
domains when we directly deploy the trained model to segment the images of
unseen (or new coming) domains. To this end, we propose a novel domain
generalization framework for the generalizable semantic segmentation task,
which enhances the generalization ability of the model from two different
views, including the training paradigm and the test strategy. Concretely, we
exploit the model-agnostic learning to simulate the domain shift problem, which
deals with the domain generalization from the training scheme perspective.
Besides, considering the data-distribution discrepancy between seen source and
unseen target domains, we develop the target-specific normalization scheme to
enhance the generalization ability. Furthermore, when images come one by one in
the test stage, we design the image-based memory bank (Image Bank in short)
with style-based selection policy to select similar images to obtain more
accurate statistics of normalization. Extensive experiments highlight that the
proposed method produces state-of-the-art performance for the domain
generalization of semantic segmentation on multiple benchmark segmentation
datasets, i.e., Cityscapes, Mapillary.
