Uncertainty-guided diffusion inpainting augments semantic segmentation data by regenerating context around hard regions and training only on preserved original pixels, yielding mIoU gains on rare classes in Cityscapes, UAVID, and BDD100K.
Blended latent diffusion.ACM transactions on graphics (TOG), 42 (4):1–11, 2023
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Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
Uncertainty-guided diffusion inpainting augments semantic segmentation data by regenerating context around hard regions and training only on preserved original pixels, yielding mIoU gains on rare classes in Cityscapes, UAVID, and BDD100K.