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AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving
This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a foundation-model adaptation framework for semantic segmentation in autonomous driving. AD-SAM combines a frozen Segment Anything Model (SAM) Vision Transformer (ViT-H) encoder with a trainable ResNet-50 encoder to integrat
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