Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation

Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a "teacher-assistant" model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the "teacher-assistant" model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model.