Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation
Ziquan Wang,
Yongsheng Zhang,
Zhenchao Zhang
et al.
Abstract: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 outpu… Show more
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