  Deep models trained on source domain lack generalization when evaluated on
unseen target domains with different data distributions. The problem becomes
even more pronounced when we have no access to target domain samples for
adaptation. In this paper, we address domain generalized semantic segmentation,
where a segmentation model is trained to be domain-invariant without using any
target domain data. Existing approaches to tackle this problem standardize data
into a unified distribution. We argue that while such a standardization
promotes global normalization, the resulting features are not discriminative
enough to get clear segmentation boundaries. To enhance separation between
categories while simultaneously promoting domain invariance, we propose a
framework including two novel modules: Semantic-Aware Normalization (SAN) and
Semantic-Aware Whitening (SAW). Specifically, SAN focuses on category-level
center alignment between features from different image styles, while SAW
enforces distributed alignment for the already center-aligned features. With
the help of SAN and SAW, we encourage both intra-category compactness and
inter-category separability. We validate our approach through extensive
experiments on widely-used datasets (i.e. GTAV, SYNTHIA, Cityscapes, Mapillary
and BDDS). Our approach shows significant improvements over existing
state-of-the-art on various backbone networks. Code is available at
https://github.com/leolyj/SAN-SAW

Computer Vision and Pattern Recognition