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相关概念视频

Visual Agnosia01:12

Visual Agnosia

176
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
176

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New generation of QSAR modeling for bee safety: predicting toxicity using graph neural networks and apistox data.

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相关实验视频

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Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
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解决视力障碍:对于图像标题解决方案的基本软件要求

Rosalvo Ferreira de Oliveira Neto1, Larissa Almeida Rocha1, Milton Pereira de Carvalho Filho2

  • 1Computer Engineering Department, Federal University of Vale do São Francisco, Juazeiro -Ba, Brazil.

Assistive technology : the official journal of RESNA
|October 30, 2024
PubMed
概括

深度学习图像标题可以改善视力受损用户的屏幕阅读器. 目前的工具需要在描述个人,物体颜色和图像上下文方面进行改进,以提高数字可访问性.

关键词:
人工智能的人工智能是人工智能.辅助技术是指辅助技术的使用.数字可访问性数字可访问性图片标题图片标题图片标题视力受损者 视力受损者

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科学领域:

  • 计算机科学 计算机科学
  • 人与计算机的交互
  • 辅助技术 辅助技术 辅助技术

背景情况:

  • 视障人士依赖于屏幕阅读器等辅助技术来与数字设备互动.
  • 深度学习和图像标题的进步为增强的音频描述提供了潜力.

研究的目的:

  • 确定针对视力受损用户量身定制的图像标题工具的关键软件要求.
  • 根据这些要求,评估现有的深度学习模型的有效性.

主要方法:

  • 定性研究包括在线调查视觉障碍用户对音频描述软件的偏好.
  • 评估当前的深度学习图像标题模型的能力.

主要成果:

  • 用户偏好突出了对个人,对象颜色和图像上下文的详细描述的需求.
  • 现有的深度学习标题模型在满足这些特定的用户定义要求方面存在局限性.
  • 完整的图像数据对于有效的标题至关重要.

结论:

  • 目前的图像标题工具并没有完全针对视力受损者进行优化.
  • 显著需要改进图像标题系统,以推进数字可访问性.
  • 未来的研究应该专注于开发包含用户定义的更丰富图像描述要求的模型.