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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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通过动态实例生成和本地值进行少数镜头像素精确的文档布局细分.

Axel De Nardin1, Silvia Zottin1, Claudio Piciarelli1

  • 1Department of Mathematics, Computer Science and Physics, Università degli Studi di Udine, Via delle Scienze 206, 33100 Udine, Italy.

International journal of neural systems
|August 11, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于文档布局细分在文化遗产研究中的一些新的快速学习框架. 人工智能方法有效地分析用最小的数据手写的文本,匹配最先进的性能.

关键词:
文件布局细分 文件布局细分几次射击的学习学习手写的文件分析,手写的文件分析.图像分割 图像细分 图像细分布局分析,布局分析.

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科学领域:

  • 数字人文学科 数字人文学科
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 文化遗产的研究越来越需要人工智能工具.
  • 文件布局细分对于分析历史文档,特别是手写文档至关重要.
  • 当前的方法需要大量的标记数据,但由于时间和专业知识的限制,这些数据往往无法获得.

研究的目的:

  • 开发一个有效的短暂学习框架,用于对文档布局进行细分.
  • 在分析文化遗产文件时应对数据稀缺的挑战.

主要方法:

  • 提出了一种用于分段文档布局的新框架.
  • 引入了两个关键组件:动态实例生成和细分精细化模块.
  • 采用了几次学习方法来最大限度地减少数据需求.

主要成果:

  • 实现了与最先进的方法可比的性能.
  • 在Diva-HisDB数据集上证明了有效性.
  • 仅需要典型培训数据的一小部分.

结论:

  • 拟议的短暂学习框架为文化遗产中的文档布局细分提供了可行的解决方案.
  • 这种方法显著减少了对大型注释数据集的需求.
  • 允许更容易访问的AI驱动的历史手写文档分析.