A dual-branch adapter module called LCA with contrast maps and pairwise training on a Unity synthetic dataset improves SAM's instance segmentation performance across lighting variations.
In: Proceedings of the IEEE international conference on computer vision
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GIF fuses geometrical image features and logical graph topology in a conditional diffusion model to generate high-quality IR drop images for chip layouts, outperforming prior ML methods on CircuitNet-N28 with SSIM 0.78, Pearson 0.95, PSNR 21.77, and NMAE 0.026.
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Lighting-aware Unified Model for Instance Segmentation
A dual-branch adapter module called LCA with contrast maps and pairwise training on a Unity synthetic dataset improves SAM's instance segmentation performance across lighting variations.
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GIF: A Conditional Multimodal Generative Framework for IR Drop Imaging in Chip Layouts
GIF fuses geometrical image features and logical graph topology in a conditional diffusion model to generate high-quality IR drop images for chip layouts, outperforming prior ML methods on CircuitNet-N28 with SSIM 0.78, Pearson 0.95, PSNR 21.77, and NMAE 0.026.