RS4D distills ViT knowledge into SSM backbones for remote sensing instance segmentation, delivering 8x fewer parameters and 9x fewer FLOPs than ViT methods while matching or exceeding accuracy on SSDD, WHU, and NWPU datasets.
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A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
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Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models
RS4D distills ViT knowledge into SSM backbones for remote sensing instance segmentation, delivering 8x fewer parameters and 9x fewer FLOPs than ViT methods while matching or exceeding accuracy on SSDD, WHU, and NWPU datasets.
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State Space Models Meet Remote Sensing: A Survey
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.