OcclusionFormer adds explicit Z-order modeling via a new SA-Z dataset and volume-rendering compositing in a diffusion transformer to resolve occlusion ambiguities in layout-grounded image synthesis.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
HFF replaces binary goodness-of-fit in Forward-Forward with hyperspherical prototypes for direct multi-class decisions, enabling single-forward-pass inference and training that scales to ImageNet while closing much of the gap to backpropagation.
DiffRGD is a plug-and-play inference-time guidance method that casts each diffusion sampling step as constrained optimization on a spherical manifold and solves it with Riemannian gradient descent to preserve the Gaussian latent structure.
citing papers explorer
-
OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation
OcclusionFormer adds explicit Z-order modeling via a new SA-Z dataset and volume-rendering compositing in a diffusion transformer to resolve occlusion ambiguities in layout-grounded image synthesis.
-
Hyperspherical Forward-Forward with Prototypical Representations
HFF replaces binary goodness-of-fit in Forward-Forward with hyperspherical prototypes for direct multi-class decisions, enabling single-forward-pass inference and training that scales to ImageNet while closing much of the gap to backpropagation.
-
DiffRGD: An Inference-Time Diffusion Guidance Through Riemannian Gradient Descent
DiffRGD is a plug-and-play inference-time guidance method that casts each diffusion sampling step as constrained optimization on a spherical manifold and solves it with Riemannian gradient descent to preserve the Gaussian latent structure.