FS-FSD regresses frequency-supervised Fourier contours for bridge defects, yielding higher polygon accuracy and better geometric quality than box, mask, or contour baselines on 3,767 UAV images with 42,346 instances.
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HSANet uses Efficient Global Attention and hybrid upsampling in a Swin-based architecture to achieve better simultaneous denoising of low-dose PET/CT images than prior methods with a compact model.
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Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours
FS-FSD regresses frequency-supervised Fourier contours for bridge defects, yielding higher polygon accuracy and better geometric quality than box, mask, or contour baselines on 3,767 UAV images with 42,346 instances.
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Hybrid Swin Attention Networks for Simultaneously Low-Dose PET and CT Denoising
HSANet uses Efficient Global Attention and hybrid upsampling in a Swin-based architecture to achieve better simultaneous denoising of low-dose PET/CT images than prior methods with a compact model.