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.
High-resolution image synthesis with latent diffusion models
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
GrOCE uses dynamic semantic graphs for online, training-free erasure of target concepts from diffusion model prompts via cluster identification and selective severing.
citing papers explorer
-
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.
-
GrOCE:Graph-Guided Online Concept Erasure for Text-to-Image Diffusion Models
GrOCE uses dynamic semantic graphs for online, training-free erasure of target concepts from diffusion model prompts via cluster identification and selective severing.