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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Collisions in Multiple Dimensions: Introduction01:05

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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The Representativeness Heuristic02:13

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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属性下降:在内容层面和超越层面上模拟以对象为中心的数据集

Yue Yao, Liang Zheng, Xiaodong Yang

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    此摘要是机器生成的。

    本研究引入了一种属性下降方法,以弥合合成数据中的内容级域差距,使其在计算机视觉任务中更现实. 优化合成数据可以提高图像分类和对象重新识别的性能.

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    相关实验视频

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 计算机图形 计算机图形

    背景情况:

    • 在计算机视觉中,合成数据和现实数据之间存在一个显著的领域差距.
    • 这种差距有两个层次:外观 (风格) 和内容 (诸如视角,照明等属性).
    • 内容级域差距的研究较少,但对于真实的数据模拟至关重要.

    研究的目的:

    • 解决合成数据生成中的内容级域差距.
    • 提出和验证一种用于优化合成数据属性的自动化方法.
    • 增强合成数据的实用性,用于各种计算机视觉应用.

    主要方法:

    • 建议采用属性下降方法来自动优化引擎属性.
    • 该方法侧重于以对象为中心的任务,其中优化信号是明确的.
    • 收集了新的合成资产 (VehicleX),并重新使用了现有的资产 (ObjectX, PersonX).

    主要成果:

    • 属性下降方法有效地减少了内容级域差距.
    • 图像分类和对象重新识别的实验显示性能有所改善.
    • 适应合成数据在仅训练,数据增强和数据集理解场景中被证明是有效的.

    结论:

    • 优化合成数据属性是弥合内容级域差距的关键.
    • 拟议的方法使合成数据在现实世界的计算机视觉任务中能够有效地使用.
    • 这种方法为生成高质量,现实的培训数据提供了可行的解决方案.