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.
arXiv preprint arXiv:2306.06370 (2023)
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SARIF combines SAM with a feedback-guided decoder and block-wise prompting on residual features to improve cross-dataset forgery localization and robustness to image corruptions.
RobustMedSAM fuses MedSAM's image encoder with RobustSAM's mask decoder and fine-tunes only the decoder on 35 medical datasets with corruptions to raise degraded-image Dice from 0.613 to 0.719.
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
citing papers explorer
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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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SARIF: Segment Anything for Robust Image Forensics
SARIF combines SAM with a feedback-guided decoder and block-wise prompting on residual features to improve cross-dataset forgery localization and robustness to image corruptions.
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RobustMedSAM: Degradation-Resilient Medical Image Segmentation via Robust Foundation Model Adaptation
RobustMedSAM fuses MedSAM's image encoder with RobustSAM's mask decoder and fine-tunes only the decoder on 35 medical datasets with corruptions to raise degraded-image Dice from 0.613 to 0.719.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
- SemiSAM-O1: Pushing the Boundary of Annotation-Efficient Medical Image Segmentation with Generalist Knowledge Fusion
- On Efficient Variants of Segment Anything Model: A Survey