The last mile of unsupervised domain adaptation (UDA) for semantic segmentation is the challenge of solving the syn-to-real domain gap. Recent UDA methods have progressed significantly, yet they often rely on strategies customized for synthetic single-source datasets (e.g., GTA5), which limits their generalisation to multi-source datasets. Conversely, synthetic multi-source datasets hold promise for advancing the last mile of UDA but remain underutilized in current research. Thus, we propose DEC, a flexible UDA framework for multi-source datasets. Following a divide-and-conquer strategy, DEC simplifies the task by categorizing semantic classes, training models for each category, and fusing their outputs by an ensemble model trained exclusively on synthetic datasets to obtain the final segmentation mask. DEC can integrate with existing UDA methods, achieving state-of-the-art performance on Cityscapes, BDD100K, and Mapillary Vistas, significantly narrowing the syn-to-real domain gap.

翻译：语义分割无监督域适应（UDA）的“最后一英里”挑战在于解决合成到真实域之间的差距。近期的UDA方法已取得显著进展，但它们通常依赖于针对合成单源数据集（如GTA5）定制的策略，这限制了其在多源数据集上的泛化能力。相反，合成多源数据集在推进UDA最后一英里方面具有潜力，但在当前研究中仍未得到充分利用。为此，我们提出DEC——一个面向多源数据集的灵活UDA框架。遵循“分而治之”策略，DEC通过将语义类别分类、为每个类别训练模型，并利用仅在合成数据集上训练的集成模型融合其输出以获取最终分割掩码，从而简化任务。DEC能够与现有UDA方法结合，在Cityscapes、BDD100K和Mapillary Vistas数据集上实现了最先进的性能，显著缩小了合成到真实域之间的差距。