DADD disentangles anatomy and disease in a latent diffusion model using a Feature Purifier, ordinal disease embeddings, and Delta Steering to synthesize controllable ulcerative colitis progression images.
Learning transferable visual models from natural language supervision
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
GeCo is a new geometry-based metric that produces dense maps of motion and structure inconsistencies in video generation by fusing residual motion and depth priors.
citing papers explorer
-
Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis
DADD disentangles anatomy and disease in a latent diffusion model using a Feature Purifier, ordinal disease embeddings, and Delta Steering to synthesize controllable ulcerative colitis progression images.
-
GeCo: Evaluating Geometric Consistency for Video Generation via Motion and Structure
GeCo is a new geometry-based metric that produces dense maps of motion and structure inconsistencies in video generation by fusing residual motion and depth priors.