MSDDA derives a closed-form optimal reverse denoising distribution for multi-objective diffusion alignment that is exactly equivalent to step-level RL fine-tuning with no approximation error.
Vila: Learning image aesthetics from user comments with vision-language pretraining
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Aes3D creates the first 3D scene aesthetic assessment dataset and a model that regresses aesthetic scores from 3DGS representations alone.
citing papers explorer
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Step-level Denoising-time Diffusion Alignment with Multiple Objectives
MSDDA derives a closed-form optimal reverse denoising distribution for multi-objective diffusion alignment that is exactly equivalent to step-level RL fine-tuning with no approximation error.
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Aes3D: Aesthetic Assessment in 3D Gaussian Splatting
Aes3D creates the first 3D scene aesthetic assessment dataset and a model that regresses aesthetic scores from 3DGS representations alone.