Domain generalized semantic segmentation (DGSS) is an essential but highly
challenging task, in which the model is trained only on source data and any
target data is not available. Previous DGSS methods can be partitioned into
augmentation-based and normalization-based ones. The former either introduces
extra biased data or only conducts channel-wise adjustments for data
augmentation, and the latter may discard beneficial visual information, both of
which lead to limited performance in DGSS. Contrarily, our method performs
inter-channel transformation and meanwhile evades domain-specific biases, thus
diversifying data and enhancing model generalization performance. Specifically,
our method consists of two modules: random image color augmentation (RICA) and
random feature distribution augmentation (RFDA). RICA converts images from RGB
to the CIELAB color model and randomizes color maps in a perception-based way
for image enhancement purposes. We further this augmentation by extending it
beyond color to feature space using a CycleGAN-based generative network, which
complements RICA and further boosts generalization capability. We conduct
extensive experiments, and the generalization results from the synthetic GTAV
and SYNTHIA to the real Cityscapes, BDDS, and Mapillary datasets show that our
method achieves state-of-the-art performance in DGSS.

本文提出了一种基于随机图像颜色增强和特征空间分布增强的域通用语义分割方法，通过对图像或特征进行性质适应性调整来增广数据集，从而提高模型的泛化性能。实验结果表明，该方法在现有方法中表现最佳。