activity
20182026
most citedESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

1.2k citations · 1.5k across the 83 of their papers we have counts for

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Showing 2026Show all

10 papers · 1 filter

cs.CV2026

Distilling Physical Priors into Streaming World Models

Liangliang Zhao, Junying Wang, Danni Yang +5

Streaming world models predict future visual states online while maintaining physically coherent dynamics over long horizons. However, their rollouts often violate basic physical c…

eess.IV2026

Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging

Michael E. Kim, Chenyu Gao, Karthik Ramadass +17

Purpose: Diffusion MRI (dMRI) provides a diverse set of quantitative measures and derived datatypes to assess white matter microstructure and macrostructure. Coupled with the incre…

cs.AI2026

Sketch Then Paint: Hierarchical Reinforcement Learning for Diffusion Multi-Modal Large Language Models

Siqi Luo, Jianghan Shen, Yi Xin +9

Diffusion Multi-Modal Large Language Models (dMLLMs) are powerful for image generation, but optimizing them through reinforcement learning (RL) remains a major challenge. One prima…

cs.CV2026

StableI2I: Spotting Unintended Changes in Image-to-Image Transition

Jiayang Li, Shuo Cao, Xiaohui Li +6

In most real-world image-to-image (I2I) scenarios, existing evaluations primarily focus on instruction following and the perceptual quality or aesthetics of the generated images. H…

cs.AI2026

An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing

Lianrui Zuo, Yihao Liu, Gaurav Rudravaram +15

Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends…

cs.CV2026

InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing

Changyao Tian, Danni Yang, Guanzhou Chen +26

Unified multimodal models (UMMs) that integrate understanding, reasoning, generation, and editing face inherent trade-offs between maintaining strong semantic comprehension and acq…

cs.AI2026

VisualDeltas: Learning Preferences from Visual Quality Perturbations

Hailiang Huang, Yihao Liu, Shengyue Guan +2

We present VisualDeltas, a lightweight preference-learning framework that extracts supervision from visual quality variations in multimodal data. By leveraging the systematic impac…

cs.CV2026

Accelerating Masked Image Generation by Learning Controlled Latent Dynamics

Kaiwen Zhu, Quansheng Zeng, Yuandong Pu +9

Masked Image Generation Models (MIGMs) have achieved great success, yet their efficiency is hampered by the multiple steps of bi-directional attention. In fact, there exists notabl…

cs.CV2026

HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding

Wenhui Liao, Hongliang Li, Pengyu Xie +15

Document parsing is a fundamental task in multimodal understanding, supporting a wide range of downstream applications such as information extraction and intelligent document analy…

cs.CV2026

Toward Generalizable Deblurring: Leveraging Massive Blur Priors with Linear Attention for Real-World Scenarios

Yuanting Gao, Shuo Cao, Xiaohui Li +3

Image deblurring has advanced rapidly with deep learning, yet most methods exhibit poor generalization beyond their training datasets, with performance dropping significantly in re…