SiamDoGe: Domain Generalizable Semantic Segmentation Using Siamese Network

Deep learning-based approaches usually suffer from performance drop on out-of-distribution samples, therefore domain generalization is often introduced to improve the robustness of deep models. Domain randomization (DR) is a common strategy to improve the generalization capability of semantic segmentation networks, however, existing DR-based algorithms require collecting auxiliary domain images to stylize the training samples. In this paper, we propose a novel domain generalizable semantic segmentation method, "SiamDoGe", which builds upon a DR approach without using auxiliary domains and employs a Siamese architecture to learn domain-agnostic features from the training dataset. Particularly, the proposed method takes two augmented versions of each training sample as input and produces the corresponding predictions in parallel. Throughout this process, the features from each branch are randomized by those from the other to enhance the feature diversity of training samples. Then the predictions produced from the two branches are enforced to be consistent conditioned on feature sensitivity. Extensive experiment results demonstrate the proposed method exhibits better generalization ability than existing state-of-the-arts across various unseen target domains.