ArXivCSExplorer
☆☆Bookmarks🏆RSSHow to UseFAQ
Built with and by Teycir Ben Soltane•
How to Use•FAQ•GitHub•arXiv.org•
Share:

~ similar to 2605.28124· 15 results

cs.LGcs.CVRecentJun 1, 2026

Measurement Geometry and Design for Trustworthy Generative Inverse Problems

Pengfei Jin, Na Li, Quanzheng Li

The paper proposes a measurement-geometry framework to quantify how well fixed measurement operators can distinguish between images generated by a prior, thereby guiding the design of more trustworthy…

View →
cs.CVcs.LGRecentJun 1, 2026

Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates

Pengfei Jin, Yiqi Tian, Kailong Fan, Bingjie Qi +1 more

The paper introduces Robust Prior Update (RPU), a module that improves the faithfulness of diffusion-based inverse solvers by stabilizing the prior update step, thereby reducing measurement-conditione…

View →
cs.CVcs.AIRecentMay 28, 2026

Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models

Kai Bian, Xucheng Guo, Bin Chen, Lingyan Ruan +3 more

The paper introduces Pocket-Dentist, an efficiency-aware benchmark and model that demonstrates that compact, smaller Vision-Language Models (VLMs) can outperform larger models in accuracy while drasti…

View →
cs.CVcs.AIcs.LGRecentMay 28, 2026

Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models

Arunkumar Kannan, Yanbo Zhang, Han Liu, Michael Baumgartner +4 more

The paper introduces a histogram-regularized latent diffusion model to synthesize highly realistic and subtype-specific pulmonary nodules in 3D CT volumes, addressing the limitations of existing metho…

View →
eess.IVcs.AIRecentMay 28, 2026

A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging

Antony Jerald, Hemant K Aggarwal, Brian Nett, Avinash Gopal +3 more

The paper proposes a unified deep learning framework to synthesize contrast-phase-specific virtual monochromatic 50 keV images from single-energy CT (SECT) data, overcoming the hardware limitations of…

View →
cs.CVcs.AIRecentJun 1, 2026

Fast and Lightweight Novel View Synthesis with Differentiable Multiplane Image

Kaidi Zhang, Guanxu Zhu

The paper proposes a fast and lightweight novel view synthesis method using a differentiable Multiplane Image (MPI) representation, achieving significant speed and size improvements over state-of-the-…

View →
cs.CVcs.AIcs.LGRecentMay 27, 2026

VDSB-GWSyn: Diffusion Schrödinger Bridge for Controllable and Anatomically Feasible Guidewire Synthesis in Coronary Angiography

Haoyuan Tang, Zhuo Zhang, Jialin Li, Shuai Xiao +1 more

The paper proposes VDSB-GWSyn, a Diffusion Schrödinger Bridge framework, to synthesize controllable and anatomically feasible guidewire images on coronary angiography (CAG) scans, significantly improv…

View →
cs.LGcs.AIcs.CVRecentMay 27, 2026

Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping

Seunghyeok Shin, Minwoo Kim, Dabin Kim, Hongki Lim

The paper introduces a novel diffusion posterior sampling method that stabilizes and accelerates data-consistent sampling by replacing hand-tuned guidance weights with a per-noise-level, curvature-gui…

View →
cs.LGcs.AIEmpiricalRecentJun 4, 2026

PC Layer: Polynomial Weight Preconditioning for Improving LLM Pre-Training

Senmiao Wang, Tiantian Fang, Haoran Zhang, Yushun Zhang +3 more

This paper proposes a preconditioning layer for stable weight conditioning in LLM training.

View →
cs.LGcs.AIEmpiricalRecentJun 4, 2026

PC Layer: Polynomial Weight Preconditioning for Improving LLM Pre-Training

Senmiao Wang, Tiantian Fang, Haoran Zhang, Yushun Zhang +3 more

This paper proposes a preconditioning layer for stable weight conditioning in LLM training.

View →
cs.CVRecentJun 1, 2026

Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models

Guangzhao He, Rundong Luo, Wei-Chiu Ma, Hadar Averbuch-Elor

The paper introduces Staged Executable Inverse Graphics (SEIG), an agentic framework that uses general-purpose Vision-Language Models (VLMs) to reconstruct editable 3D scenes directly into executable…

View →
cs.CVRecentJun 1, 2026

LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models

Lu Liu, Huiyu Duan, Chenxin Zhu, Jintong Lu +5 more

The paper introduces LL-Bench, a comprehensive benchmark for evaluating large-scale generative models on low-level vision tasks, and proposes LL-Score, an MLLM-based evaluator that better aligns quali…

View →
cs.CVcs.AIcs.CLRecentMay 29, 2026

Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation

Tom Maye-Lasserre, Yitong Li, Bailiang Jian, Morteza Ghahremani +2 more

The paper addresses 'Template Collapse' in 3D CT report generation—where models generate generic reports—by proposing CLarGen, a decoupled framework that significantly improves clinical accuracy and d…

View →
cs.CLcs.AIRecentMay 31, 2026

DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs

Longxuan Yu, Yunshu Wu, Yu Fu, Siheng Xiong +4 more

The paper introduces DSL-LLaDA, a method that lightly adapts a pre-trained masked diffusion language model to perform continuous denoising in embedding space, significantly improving text generation q…

View →
cs.LGstat.MLRecentJun 2, 2026

Conformal Language Modeling via Posterior Sampling

Nicolas Emmenegger, Theo X. Olausson, Armando Solar-Lezama, Chara Podimata

The paper proposes sampling directly from approximations of an LLM posterior, conditioned on high-scoring regions, to generate more coherent and useful text compared to existing post-hoc hallucination…

View →