單一域泛化的目的在於僅使用單一源域的設定下學習出具有域泛化性的模型。為了避免模型過擬合在源域的問題，過往文獻多專注在透過域擴增的方法來學習域泛化之特徵。因此域的多樣性對於模型的泛化能力至關重要。在本文中，我們針對單一域泛化語意分割任務提出一個新穎的雙重域擴增框架來增加域的多樣性。我們特別設計圖像等級擴增模塊以及類別等級擴增模塊來分別擴展合成圖片與各類別特徵的多樣性。
接著我們再基於原圖與合成圖像設計域泛化特徵學習，並利用大型預訓練模型來約束分割模型能學習具有代表性的特徵。在多個語意分割基準上的實驗結果顯示我們所提出方法在有效性和性能方面優越於過往的文獻。

The goal of single-domain generalization is to learn a domain-generalized model from only one single source domain. To avoid overfitting to the source domain, recent research focused on domain augmentation for learning domain generalized features. Therefore, domain diversity is indeed crucial to the generalization ability of the model.
In this paper, we propose a novel dual-level domain augmentation framework to enrich the domain diversity for single-domain generalized semantic segmentation.
We specifically devise an Image-Level and a Class-Level Augmentation Module (IAM and CAM) to enlarge the diversity of augmented images and per-class features, respectively. From the original and augmented data, we then design a Domain-Generalized Feature Learning to learn representative features regularized by a large-scale pre-trained model. Experimental results on semantic segmentation benchmarks demonstrate the effectiveness and outperformance of the proposed method over previous work.

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