Domain generalization (DG) aims to learn a model on several source domains,
hoping that the model can generalize well to unseen target domains. The
distribution shift between domains contains the covariate shift and conditional
shift, both of which the model must be able to handle for better
generalizability. In this paper, a novel DG method is proposed to deal with the
distribution shift via Visual Alignment and Uncertainty-guided belief Ensemble
(VAUE). Specifically, for the covariate shift, a visual alignment module is
designed to align the distribution of image style to a common empirical
Gaussian distribution so that the covariate shift can be eliminated in the
visual space. For the conditional shift, we adopt an uncertainty-guided belief
ensemble strategy based on the subjective logic and Dempster-Shafer theory. The
conditional distribution given a test sample is estimated by the dynamic
combination of that of source domains. Comprehensive experiments are conducted
to demonstrate the superior performance of the proposed method on four widely
used datasets, i.e., Office-Home, VLCS, TerraIncognita, and PACS.

本文提出一种新颖的领域泛化方法，通过视觉对齐和基于不确定性引导的置信度集成 (VAUE) 来处理分布偏移，在 4 个广泛使用的数据集上展示了其卓越的性能。