Domain generalized semantic segmentation is an essential computer vision task, for which models only leverage source data to learn the capability of generalized semantic segmentation towards the unseen target domains. Previous works typically address this challenge by global style randomization or feature regularization. In this paper, we argue that given the observation that different local semantic regions perform different visual characteristics from the source domain to the target domain, methods focusing on global operations are hard to capture such regional discrepancies, thus failing to construct domain-invariant representations with the consistency from local to global level. Therefore, we propose the Semantic-Rearrangement-based Multi-Level Alignment (SRMA) to overcome this problem. SRMA first incorporates a Semantic Rearrangement Module (SRM), which conducts semantic region randomization to enhance the diversity of the source domain sufficiently. A Multi-Level Alignment module (MLA) is subsequently proposed with the help of such diversity to establish the global-regional-local consistent domain-invariant representations. By aligning features across randomized samples with domain-neutral knowledge at multiple levels, SRMA provides a more robust way to handle the source-target domain gap. Extensive experiments demonstrate the superiority of SRMA over the current state-of-the-art works on various benchmarks.

提出了基于语义重排的多级对齐（SRMA）方法，通过语义区域随机化增强源领域的多样性，并通过多级对齐模块建立全局-区域-局部一致的域不变表示，有效解决了源目标领域差距问题。实验证明了SRMA方法在各项基准测试上的优越性。

基于语义重新排列的多层次对齐领域泛化分割