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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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相关实验视频

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基于图形的视觉转换器,可用于从头开始对小数据集进行培训.

Peng Li1, Lu Huang2,3, Jin Li2

  • 1Emergency Department, Yantaishan Hospital, Yantai, Shandong, 264008, China.

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PubMed
概括

基于图形的视觉转换器 (GvTs) 通过结合诱导偏差来弥合小型数据集的性能差距. 在没有预先培训的情况下,GvT的表现优于标准的视觉转换器 (ViT).

关键词:
图形的卷积可以表示.图形共享 - 图形共享图像的分类图像的分类.专注于自己的注意力视觉变压器 视觉变压器

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 视觉转换器 (ViT) 在大规模图像分类方面表现出色,但在较小的数据集上,由于缺乏诱导偏差,与卷积神经网络 (CNN) 相比,其表现不佳.
  • 这种性能差异凸显了对架构修改的需求,以在数据有限的场景中提高ViT效率.

研究的目的:

  • 引入基于图形的视觉转换器 (GvT),旨在通过集成基于图形的机制来提高小数据集的ViT性能.
  • 解决标准ViT在捕获局部空间信息方面的局限性,并缓解注意力机制中的低级瓶.

主要方法:

  • 拟议的GvT采用了对查询和密钥的图形卷积投影,在每个块内使用空间邻矩阵.
  • 图形卷积用于生成值,并应用说话头技术来克服注意力头的低级瓶.
  • 在中间块之间集成了图形聚合,以减少令牌数量并增强语义信息聚合.

主要成果:

  • 在小数据集上,GvT表现出与深度CNN相美或更高的性能.
  • GvT模型超过了标准ViT的性能,这些ViT没有在大型数据集上进行预训练.

结论:

  • GvT架构有效地引入了诱导偏差,显著提高了视觉转换器在小数据集上的性能.
  • 在训练数据有限的情况下,GvT为传统的CNN和标准ViT提供了一个有希望的替代方案.