arXiv Computer Vision By Mingxin Liu, Ziqian Fan, Zhaokai Wang, Leyao Gu, Zirun Zhu, Yiguo He, Yuchen Yang, Changyao Tian, Xiangyu Zhao, Ning Liao, Shaofeng Zhang, Qibing Ren, Zhihang Zhong, Xuanhe Zhou, Junchi Yan, Xue Yang

GRADE: Benchmarking Discipline-Informed Reasoning in Image Editing

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arXiv Computer Vision
Sep 1

DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing

arXiv:2607.02290v2 Announce Type: replace Abstract: Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a kno...

By Zhaokai Wang, Mingxin Liu, Zirun Zhu, Ziqian Fan, Yiguo He, Mohan Zhang, Leyao Gu, Yan Li, Xiangyu Zhao, Ning Liao, Shaofeng Zhang, Xuanhe Zhou, Zhihang Zhong, Xue Yang
arXiv AI
Aug 26

Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility

The paper presents a systematic study of scientific image synthesis, comparing pixel‑based generation and programmatic approaches. It introduces ImgCoder, a logic‑driven framework that follows an "understand‑plan‑code" workflow to enhance structural precision, and SciGenBench, a benchmark that evaluates images for information utility and logical validity. The authors find that pixel‑based models exhibit systematic failure modes and that fine‑tuning large multimodal models on rigorously verified synthetic images consistently improves downstream reasoning performance.

By Honglin Lin, Zheng Liu, Chonghan Qin, Qizhi Pei, Yu Li, Zhanping Zhong, Xin Gao, Yanfeng Wang, Conghui He, Lijun Wu
Hugging Face Trending Papers
Jun 23

S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing

We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose image generation models, scientific image tasks require not only high-fidelity synthesis, but also robust understanding of scientific semantics, structural relations, domain knowledge, and task intent.

arXiv AI
Aug 25

ReasonEdit: Editing Vision-Language Models using Human Reasoning

ReasonEdit is a new editor for vision‑language models that allows users to provide reasoning explanations during the editing process. It stores human reasoning in a codebook and retrieves relevant facts at inference time using a topology‑balanced multimodal embedding approach inspired by network science. Experiments on four VLMs and multiple rationale‑based visual question answering datasets show that incorporating human reasoning leads to state‑of‑the‑art editing performance and better generalization.

By Jiaxing Qiu, Kaihua Hou, Roxana Daneshjou, Ahmed Alaa, Thomas Hartvigsen
arXiv Computer Vision
2d ago

SciGen-Verifier: A Multimodal Reasoner for Explainable Verification in Scientific Image Generation

arXiv:2609.33399v2 Announce Type: replace Abstract: In realistic education, a solution is often expressed not only in words but in a drawing--a circuit, a geometric construction, a function plot--and...

By Jiali Chen, Zhengteng Lin, Zuqi Wang, Shirong Lin, Xi Yu, Xusen Hei, DingBa Fu, Jiayuan Xie, Yi Cai
arXiv AI
Jun 2

TECCI: Tricky Edits of Collected and Curated Images

arXiv:2606. 01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction following, minimally editing the source image, and ensuring high visual quality.

By Aishwarya Agrawal, Roy Hirsch, Yasumasa Onoe, Sherry Ben, Jason Baldridge