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English(EN) DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing

新数据集DisciplineGen-1M助力学术内容的AI视觉生成

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了DisciplineGen-1M，这是一个大规模数据集，旨在提高AI模型在生成和编辑知识密集型视觉内容方面的准确性。该数据集包含跨越数学、物理和计算机科学等十个学科的120万个样本，并通过结合矢量图形渲染、基于OCR的编辑和程序化合成的框架构建而成。使用该数据集进行的实验在GenExam和GRADE等特定学科的基准测试中显示出显著的改进，这表明结构化的学术视觉数据对于推动AI能力超越美学吸引力，实现可验证的、基于知识的视觉创作至关重要。 AI

影响 增强AI在跨学术学科生成和编辑准确的、基于知识的视觉内容的能力。

排序理由 该项目是一篇介绍新数据集和模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新数据集DisciplineGen-1M助力学术内容的AI视觉生成

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该项目是一篇介绍新数据集和模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Zhaokai Wang, Mingxin Liu, Zirun Zhu, Ziqian Fan, Yiguo He, Mohan Zhang, Leyao Gu, Xiangyu Zhao, Ning Liao, Shaofeng Zhang, Xuanhe Zhou, Zhihang Zhong, Junchi Yan, Xue Yang ·

    DisciplineGen-1M：一个用于多学科视觉生成和编辑的大规模数据集

    arXiv:2607.02290v1 Announce Type: new Abstract: Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a knowledge-intensive diagram whose correctness depends on disciplinary concepts, symbolic…

  2. arXiv cs.CV TIER_1 English(EN) · Xue Yang ·

    DisciplineGen-1M：面向多学科视觉生成与编辑的大规模数据集

    Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a knowledge-intensive diagram whose correctness depends on disciplinary concepts, symbolic structure, and precise spatial relations. We in…

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