Abstract
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.
Abstract (translated)
开放集领域自适应语义分割（OSDA-SS）提出了一项重大挑战，即需要同时处理已知类别和未知类别的领域适应问题。现有方法试图在一个统一的阶段解决这两个任务。然而，我们对这种设计提出了质疑，因为已知与未知类之间的标注不平衡往往会带来负面迁移效应，导致已知类学习效果下降及对未知类的学习不足。为了解决这些问题，我们提出了一种名为SATS（分离-适应训练策略）的方法，该方法通过两个连续的步骤来解决OSDA-SS问题：已知/未知分离和识别未知类别的领域自适应。通过提供给模型更为准确且对齐良好的未知类别信息，我们的方法能够确保模型对已知和未知类别的判别特征进行平衡学习，并推动模型发现真正的未知对象。 此外，我们还提出了一种创新的数据增强方法——硬未知探索（hard unknown exploration），这种方法使模型面临更具挑战性的未知情况，从而增强了其捕捉目标未知类别全面理解的能力。我们在公开的OSDA-SS基准测试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数据集上的H-Score提升了3.85%，在SYNTHIA到Cityscapes数据集上的提升达到了18.64%，优于现有的最先进方法。
URL
https://arxiv.org/abs/2601.01439
