Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) presents a significant challenge, as it requires both domain adaptation for known classes and the distinction of unknowns. Existing methods attempt to address both tasks within a single unified stage. We question this design, as the annotation imbalance between known and unknown classes often leads to negative transfer of known classes and underfitting for unknowns. To overcome these issues, we propose SATS, a Separating-then-Adapting Training Strategy, which addresses OSDA-SS through two sequential steps: known/unknown separation and unknown-aware domain adaptation. By providing the model with more accurate and well-aligned unknown classes, our method ensures a balanced learning of discriminative features for both known and unknown classes, steering the model toward discovering truly unknown objects. Additionally, we present hard unknown exploration, an innovative data augmentation method that exposes the model to more challenging unknowns, strengthening its ability to capture more comprehensive understanding of target unknowns. We evaluate our method on public OSDA-SS benchmarks. Experimental results demonstrate that our method achieves a substantial advancement, with a +3.85% H-Score improvement for GTA5-to-Cityscapes and +18.64% for SYNTHIA-to-Cityscapes, outperforming previous state-of-the-art methods.

翻译：开放集域自适应语义分割（OSDA-SS）提出了一个重大挑战，因为它既需要对已知类别进行域适应，又需要区分未知类别。现有方法试图在单一统一阶段内同时处理这两项任务。我们质疑这种设计，因为已知类别与未知类别之间的标注不平衡往往导致已知类别的负迁移以及未知类别的欠拟合。为克服这些问题，我们提出了SATS（分离后自适应训练策略），该策略通过两个顺序步骤解决OSDA-SS问题：已知/未知分离和未知感知域适应。通过为模型提供更准确且对齐良好的未知类别，我们的方法确保了对已知和未知类别判别特征的平衡学习，引导模型发现真正的未知对象。此外，我们提出了硬未知探索——一种创新的数据增强方法，使模型暴露于更具挑战性的未知样本，从而增强其捕获对目标未知类别更全面理解的能力。我们在公开的OSDA-SS基准上评估了我们的方法。实验结果表明，我们的方法取得了显著进步，在GTA5到Cityscapes上实现了+3.85%的H-Score提升，在SYNTHIA到Cityscapes上实现了+18.64%的提升，超越了先前的最先进方法。