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 this https URL

提出了一种用于语义分割的开放域适应方法，在目标域中包含未知类别，通过构建边界和未知形状感知的开放域域自适应模型 (BUS)，利用新颖的膨胀腐蚀对比损失函数来准确识别已知和未知类别的边界，并提出了一种新的领域混合增强方法（OpenReMix），此方法指导模型学习域和尺寸不变特征，以改善已知和未知类别的形状检测，通过广泛实验表明，与之前的方法相比，BUS 在具有挑战性的 OSDA-SS 场景中有效地检测未知类别。