Open-Set Domain Adaptation for Semantic Segmentation

Seun-An Choe, Ah-Hyung Shin, Keon-Hee Park, Jinwoo Choi, Gyeong-Moon Park; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 23943-23953

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.