Semantic segmentation plays a critical role in enabling intelligent vehicles
to comprehend their surrounding environments. However, deep learning-based
methods usually perform poorly in domain shift scenarios due to the lack of
labeled data for training. Unsupervised domain adaptation (UDA) techniques have
emerged to bridge the gap across different driving scenes and enhance model
performance on unlabeled target environments. Although self-training UDA
methods have achieved state-of-the-art results, the challenge of generating
precise pseudo-labels persists. These pseudo-labels tend to favor majority
classes, consequently sacrificing the performance of rare classes or small
objects like traffic lights and signs. To address this challenge, we introduce
SAM4UDASS, a novel approach that incorporates the Segment Anything Model (SAM)
into self-training UDA methods for refining pseudo-labels. It involves
Semantic-Guided Mask Labeling, which assigns semantic labels to unlabeled SAM
masks using UDA pseudo-labels. Furthermore, we devise fusion strategies aimed
at mitigating semantic granularity inconsistency between SAM masks and the
target domain. SAM4UDASS innovatively integrate SAM with UDA for semantic
segmentation in driving scenes and seamlessly complements existing
self-training UDA methodologies. Extensive experiments on synthetic-to-real and
normal-to-adverse driving datasets demonstrate its effectiveness. It brings
more than 3% mIoU gains on GTA5-to-Cityscapes, SYNTHIA-to-Cityscapes, and
Cityscapes-to-ACDC when using DAFormer and achieves SOTA when using MIC. The
code will be available at this https URL

SAM4UDASS 是一种新颖方法，通过将 Segment Anything Model（SAM）与自训练的无监督域自适应方法相结合，用于改进伪标签和解决类别不平衡问题，在驾驶场景中实现语义分割，并与现有自训练的无监督域自适应方法无缝衔接。经过在合成到真实和正常到恶劣的驾驶数据集上的广泛实验，证明了其有效性，使用 DAFormer 在 GTA5 到 Cityscapes、SYNTHIA 到 Cityscapes 和 Cityscapes 到 ACDC 上获得超过 3% mIoU 的提升，并在使用 MIC 时达到了 SOTA 水平。