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相关概念视频

Vision01:24

Vision

59.4K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
59.4K
Concepts and Prototypes01:24

Concepts and Prototypes

508
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
508
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

542
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Visual System01:26

Visual System

1.7K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
1.7K
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

3.5K
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
3.5K
Color Vision01:24

Color Vision

1.4K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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相关实验视频

Updated: Jan 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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通过视觉语言模型增强基于概念的解释.

Imran Hossain1, Ghada Zamzmi1, Peter Mouton2

  • 1Computer Science and Engineering University of South Florida Tampa, Florida, USA.

Proceedings. IEEE International Symposium on Computer-Based Medical Systems
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用视觉语言模型 (VLMs) 的新方法,以简化用于解释AI模型的概念选择. 该方法产生可理解的图像概念,与自然概念相匹配,没有额外的成本.

关键词:
这是一个基于概念的XAI.深度神经网络 深度神经网络可以解释的可解释性.生成型的人工智能

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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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相关实验视频

Last Updated: Jan 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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科学领域:

  • 人工智能的人工智能
  • 机器学习可解释性 机器学习可解释性
  • 计算机视觉 计算机视觉

背景情况:

  • 基于概念的方法对于AI模型的可解释性至关重要,但通常需要专家知识来识别概念.
  • 现有的概念选择方法可能很复杂,对于非专家来说是难以接近的,阻碍了更广泛的模型理解.
  • 后期解释方法旨在在培训后澄清模型决策过程.

研究的目的:

  • 开发一种新的,简化的方法来识别和生成人类可读的概念,以解释AI模型的行为.
  • 利用最先进的视觉语言模型 (VLMs) 进行自动化概念选择和转换.
  • 量化生成概念的重要性,并评估它们在解释模型预测方面的有效性.

主要方法:

  • 使用视觉语言模型 (VLM) 选择描述数据集类的相关文本概念.
  • 使用文本到图像模型将选定的文本概念转化为可视化理解的图像概念.
  • 应用了定向导数和概念激活向量来测量产生的概念对模型输出的影响.
  • 在新生儿疼痛分类任务中评估了该方法.

主要成果:

  • VLM成功地为非专家生成了连贯,有意义和易于理解的图像概念.
  • 生成的概念表现出与手动选择的自然图像概念相当的性能.
  • 该方法有效地解释了目标网络的行为.
  • 为了生成解释性概念,不需要额外的注释成本.

结论:

  • 提出的基于VLM的方法大大简化了用于AI模型解释的概念选择.
  • 这种方法通过生成直观的视觉概念,使得非专家更容易获得模型的解释性.
  • 该方法为传统的基于概念的解释技术提供了具有成本效益和效率的替代方案.