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.

提出了 DEC，这是一个灵活的多源数据集的 UDA 框架，通过将语义类别进行分类，并使用仅在合成数据集上训练的集成模型将它们的输出融合，来解决研究中的合成多源数据集的挑战，以获得最终的分割掩模，并可以与现有的 UDA 方法相结合，在 Cityscapes、BDD100K 和 Mapillary Vistas 上实现了最先进的性能。