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20242026
most citedRealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

2 citations · 2 across the 7 of their papers we have counts for

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cs.CV2026

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics

Bowei Liu, Zheng Lu, Yuhan Bian +8

Recent advances in video generation models have significantly improved the realism of synthetic videos, blurring the boundary between generated and authentic content and raising co…

cs.CV2025

Generative Universal Verifier as Multimodal Meta-Reasoner

Xinchen Zhang, Xiaoying Zhang, Youbin Wu +5

We introduce Generative Universal Verifier, a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, pr…

cs.CV2025

PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning

Yizhen Zhang, Yang Ding, Shuoshuo Zhang +9

Inspired by the impressive reasoning capabilities demonstrated by reinforcement learning approaches like DeepSeek-R1, recent emerging research has begun exploring the use of reinfo…

cs.CV2025

MMaDA: Multimodal Large Diffusion Language Models

Ling Yang, Ye Tian, Bowen Li +4

We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal un…

cs.CV2025

Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening

Ye Tian, Ling Yang, Xinchen Zhang +3

We propose Diffusion-Sharpening, a fine-tuning approach that enhances downstream alignment by optimizing sampling trajectories. Existing RL-based fine-tuning methods focus on singl…

cs.CV2025

HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation

Ling Yang, Xinchen Zhang, Ye Tian +4

The remarkable success of the autoregressive paradigm has made significant advancement in Multimodal Large Language Models (MLLMs), with powerful models like Show-o, Transfusion an…

cs.CV2024

IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation

Xinchen Zhang, Ling Yang, Guohao Li +6

Advanced diffusion models like RPG, Stable Diffusion 3 and FLUX have made notable strides in compositional text-to-image generation. However, these methods typically exhibit distin…

cs.CV2024★ 2 cited

RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

Xinchen Zhang, Ling Yang, Yaqi Cai +8

Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositio…