  In this paper, we study the task of synthetic-to-real domain generalized
semantic segmentation, which aims to learn a model that is robust to unseen
real-world scenes using only synthetic data. The large domain shift between
synthetic and real-world data, including the limited source environmental
variations and the large distribution gap between synthetic and real-world
data, significantly hinders the model performance on unseen real-world scenes.
In this work, we propose the Style-HAllucinated Dual consistEncy learning
(SHADE) framework to handle such domain shift. Specifically, SHADE is
constructed based on two consistency constraints, Style Consistency (SC) and
Retrospection Consistency (RC). SC enriches the source situations and
encourages the model to learn consistent representation across
style-diversified samples. RC leverages real-world knowledge to prevent the
model from overfitting to synthetic data and thus largely keeps the
representation consistent between the synthetic and real-world models.
Furthermore, we present a novel style hallucination module (SHM) to generate
style-diversified samples that are essential to consistency learning. SHM
selects basis styles from the source distribution, enabling the model to
dynamically generate diverse and realistic samples during training. Experiments
show that our SHADE yields significant improvement and outperforms
state-of-the-art methods by 5.05% and 8.35% on the average mIoU of three
real-world datasets on single- and multi-source settings, respectively.
