  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.
