HERA is a select-regularize-calibrate framework adapting frozen vision foundation models for cross-domain few-shot semantic segmentation via hierarchical layer selection with ETR, prior-guided regularization, and pixelwise adaptive calibration, reporting over 4.1 mIoU gains.
The pascal visual object classes (voc) challenge.International journal of computer vision, 88(2):303–338
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A differentiable fuzzy logic module called DKU discovers implicit concepts from image classification supervision and applies logical adjustments to improve class probabilities on PASCAL-VOC, COCO, and MedMNIST.
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Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation
HERA is a select-regularize-calibrate framework adapting frozen vision foundation models for cross-domain few-shot semantic segmentation via hierarchical layer selection with ETR, prior-guided regularization, and pixelwise adaptive calibration, reporting over 4.1 mIoU gains.
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Learning to Reason: Targeted Knowledge Discovery and Fuzzy Logic Update for Robust Image Recognition
A differentiable fuzzy logic module called DKU discovers implicit concepts from image classification supervision and applies logical adjustments to improve class probabilities on PASCAL-VOC, COCO, and MedMNIST.