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相关概念视频

Synesthesia01:27

Synesthesia

128
Synesthesia is a remarkable condition where stimulation of one sensory or cognitive pathway leads to automatic, involuntary experiences in a second sensory or cognitive pathway. People with synesthesia experience a blending or crossing of their senses, such as sight and sound, leading to cross-modal sensations. In this condition, the stimulation of one sense, such as hearing a number or musical note, triggers an experience of another sense, like sensing a specific color, taste, or smell. People...
128
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

661
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.
661
State Space Representation01:27

State Space Representation

209
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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双向视觉触觉交叉模式生成使用隐性特征空间流动模型.

Yu Fang1, Xuehe Zhang1, Wenqiang Xu2

  • 1State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 2, Yikuang Street, Nangang District, Harbin, 150001, Heilongjiang, China.

Neural networks : the official journal of the International Neural Network Society
|December 30, 2023
PubMed
概括

这项研究引入了一种新的方法,用于使用单一模型进行双向视觉触觉映射. 该方法实现了高数据相似性和分类准确性,推进了跨模式的人工智能研究.

关键词:
跨模式的交叉方式.深度学习是一种深度学习.流量模型的流量模型.视觉触觉数据 视觉触觉数据

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉
  • 触觉学是指触觉学是指触觉学.

背景情况:

  • 现有的交叉模式映射研究主要集中在单向方法上.
  • 人类大脑研究强调视觉触觉交叉模态双向映射.
  • 在AI中,视觉和触觉数据之间的双向映射存在有限的探索.

研究的目的:

  • 开发一种用于视觉和触觉数据之间的双向映射的新方法.
  • 为了使单个模型能够执行跨模态双向映射.
  • 解决现有研究中单向映射的局限性.

主要方法:

  • 使用单独的变异自编码器 (VAE) 模型用于视觉和触觉数据.
  • 引入了一个基于 VAE 潜伏特征空间构建的条件流模型.
  • 通过使用统一模型,实现视觉和触觉数据之间的交叉模式双向映射.

主要成果:

  • 在生成的视觉 (0.58) 和触觉 (0.80) 数据中实现了高的结构相似度指数 (SSIM).
  • 在生成的数据上表现出极好的分类准确性 (视觉:91.60%,触觉:88.05%).
  • 在生成数据和语言之间获得了显著的零射击分类准确性 (视觉:44.49%,触觉:45.03%).

结论:

  • 提出的方法是第一个使用单一模型实现双向视觉触觉映射的方法.
  • 该方法在数据生成和跨模式分类方面表现出强的表现.
  • 该模型和代码将公开发布,以促进进一步的研究.