AgenticVBench evaluates frontier VLMs on 100 real-world video post-production tasks across four families, with the best agent stack scoring just over 30% versus human experts.
Solving inverse problems with latent diffusion models via hard data consistency.arXiv preprint arXiv:2307.08123,
9 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
PGM framework links diffusion to proximal regularization for closed-form Moreau-score sampling in Bayesian inverse problems, learned only from prior samples.
ΔLPS is a gradient-guided discrete posterior sampler for inverse problems that works with masked or uniform discrete diffusion priors and outperforms prior discrete methods on image restoration tasks.
A denoising diffusion model trained only on synthetic brain phantoms, with explicit physics-based data consistency, produces high-accuracy quantitative T1/T2/PD maps from fourfold-accelerated MuPa-ZTE acquisitions and generalizes to real scans.
ART-VITON uses residual prior initialization and artifact-free measurement-guided sampling with data consistency, frequency correction, and periodic denoising to generate artifact-free virtual try-on images on VITON-HD, DressCode, and SHHQ-1.0.
PPM derives a tractable gradient for exact KL optimization in diffusion variational inversion to achieve unbiased posterior matching without heuristic approximations.
Longwang enables zero-shot downscaling of global precipitation to daily 10 km resolution from monthly 100 km data by learning a context-conditioned latent generative prior and using posterior sampling with a physical observation operator.
CDPA scales diffusion-based reconstruction to large 3D volumes by conditioning 2D models on initial 3D reconstructions plus data-consistency alignment, delivering state-of-the-art results on synthetic and real CBCT data.
NPN introduces a neural-network-based regularization that promotes reconstructions lying in a low-dimensional projection of the sensing operator's null-space, with claimed theoretical guarantees and improved empirical performance across compressive sensing, deblurring, super-resolution, CT, and MRI.
citing papers explorer
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AgenticVBench: Can AI Agents Complete Real-World Post-Production Tasks?
AgenticVBench evaluates frontier VLMs on 100 real-world video post-production tasks across four families, with the best agent stack scoring just over 30% versus human experts.
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Proximal-Based Generative Modeling for Bayesian Inverse Problems
PGM framework links diffusion to proximal regularization for closed-form Moreau-score sampling in Bayesian inverse problems, learned only from prior samples.
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Discrete Langevin-Inspired Posterior Sampling
ΔLPS is a gradient-guided discrete posterior sampler for inverse problems that works with masked or uniform discrete diffusion priors and outperforms prior discrete methods on image restoration tasks.
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q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Models
A denoising diffusion model trained only on synthetic brain phantoms, with explicit physics-based data consistency, produces high-accuracy quantitative T1/T2/PD maps from fourfold-accelerated MuPa-ZTE acquisitions and generalizes to real scans.
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ART-VITON: Measurement-Guided Latent Diffusion for Artifact-Free Virtual Try-On
ART-VITON uses residual prior initialization and artifact-free measurement-guided sampling with data consistency, frequency correction, and periodic denoising to generate artifact-free virtual try-on images on VITON-HD, DressCode, and SHHQ-1.0.
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Unbiased Diffusion Variational Inversion via Principled Posterior Matching
PPM derives a tractable gradient for exact KL optimization in diffusion variational inversion to achieve unbiased posterior matching without heuristic approximations.
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Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior
Longwang enables zero-shot downscaling of global precipitation to daily 10 km resolution from monthly 100 km data by learning a context-conditioned latent generative prior and using posterior sampling with a physical observation operator.
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Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
CDPA scales diffusion-based reconstruction to large 3D volumes by conditioning 2D models on initial 3D reconstructions plus data-consistency alignment, delivering state-of-the-art results on synthetic and real CBCT data.
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NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems
NPN introduces a neural-network-based regularization that promotes reconstructions lying in a low-dimensional projection of the sensing operator's null-space, with claimed theoretical guarantees and improved empirical performance across compressive sensing, deblurring, super-resolution, CT, and MRI.