Style Blind Domain Generalized Semantic Segmentation via Covariance
  Alignment and Semantic Consistence Contrastive Learning
Woo-Jin Ahn Geun-Yeong Yang Hyun-Duck Choi Myo-Taeg Lim
Deep learning models for semantic segmentation often experience performance
degradation when deployed to unseen target domains unidentified during the
training phase. This is mainly due to variations in image texture (\ie style)
from different data sources. To tackle this challenge, existing domain
generalized semantic segmentation (DGSS) methods attempt to remove style
variations from the feature. However, these approaches struggle with the
entanglement of style and content, which may lead to the unintentional removal
of crucial content information, causing performance degradation. This study
addresses this limitation by proposing BlindNet, a novel DGSS approach that
blinds the style without external modules or datasets. The main idea behind our
proposed approach is to alleviate the effect of style in the encoder whilst
facilitating robust segmentation in the decoder. To achieve this, BlindNet
comprises two key components: covariance alignment and semantic consistency
contrastive learning. Specifically, the covariance alignment trains the encoder
to uniformly recognize various styles and preserve the content information of
the feature, rather than removing the style-sensitive factor. Meanwhile,
semantic consistency contrastive learning enables the decoder to construct
discriminative class embedding space and disentangles features that are
vulnerable to misclassification. Through extensive experiments, our approach
outperforms existing DGSS methods, exhibiting robustness and superior
performance for semantic segmentation on unseen target domains.