Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, current UDA methods typically assume a shared label space between source and target, limiting their applicability in real-world scenarios where novel categories may emerge in the target domain. In this paper, we introduce Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) for the first time, where the target domain includes unknown classes. We identify two major problems in the OSDA-SS scenario as follows: 1) the existing UDA methods struggle to predict the exact boundary of the unknown classes, and 2) they fail to accurately predict the shape of the unknown classes. To address these issues, we propose Boundary and Unknown Shape-Aware open-set domain adaptation, coined BUS. Our BUS can accurately discern the boundaries between known and unknown classes in a contrastive manner using a novel dilation-erosion-based contrastive loss. In addition, we propose OpenReMix, a new domain mixing augmentation method that guides our model to effectively learn domain and size-invariant features for improving the shape detection of the known and unknown classes. Through extensive experiments, we demonstrate that our proposed BUS effectively detects unknown classes in the challenging OSDA-SS scenario compared to the previous methods by a large margin. The code is available at https://github.com/KHU-AGI/BUS.

翻译：语义分割的无监督领域自适应旨在将像素级知识从带标签的源领域迁移到无标签的目标领域。然而，现有的无监督领域自适应方法通常假设源领域与目标领域共享相同的标签空间，这限制了其在现实场景中的应用，因为目标领域中可能出现新的类别。本文首次提出开放集语义分割领域自适应，其中目标领域包含未知类别。我们指出该场景下存在两个主要问题：1）现有无监督领域自适应方法难以准确预测未知类别的边界；2）这些方法无法精确预测未知类别的形状。为解决这些问题，我们提出了边界与未知形状感知的开放集领域自适应方法，简称BUS。该方法通过一种新颖的基于膨胀-腐蚀的对比损失，以对比方式精确区分已知与未知类别之间的边界。此外，我们提出了OpenReMix——一种新的领域混合增强方法，能够引导模型有效学习领域和尺度不变特征，从而提升已知与未知类别的形状检测能力。通过大量实验，我们证明所提出的BUS方法在具有挑战性的开放集语义分割场景中，较以往方法能显著提升未知类别的检测性能。代码已发布于https://github.com/KHU-AGI/BUS。