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相关概念视频

The Anchoring-and-Adjustment Heuristic01:25

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Attribution Theory00:56

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
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在带有因果提示符的图像标题中推进因果干预.

Youngjoon Yu, Yeonju Kim, Yong Man Ro

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    此摘要是机器生成的。

    这项研究介绍了因果提示网络 (CPNet) 以改进图像标题. CPNet通过完善特征表示来增强因果干预,在基准数据集上实现最先进的性能.

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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
    • 自然语言处理自然语言处理.

    背景情况:

    • 图像标题模型经常与因果推理作斗争.
    • 在图像标题中存在因果干预的现有方法具有局限性.

    研究的目的:

    • 引入一个新的因果提示网络 (CPNet),以加强图像标题的因果干预.
    • 提高因果干预技术在图像标题的性能和灵活性.

    主要方法:

    • 开发了一个因果提示网络 (CPNet),利用功能空间中的视觉提示工程.
    • 采用了两个视觉提示:感兴趣的因果区域 (RoI) 提示 (CRP) 和因果匹配提示 (CMP).
    • CRP增强了 RoI 特征与已解散的因果特征;CMP增强了混因素的上下文表示.

    主要成果:

    • 与最先进的 (SOTA) 图像标题方法相比,CPNet表现出更高的性能.
    • 在MS-COCO和Flickr30k数据集上进行了实验,并使用Karpathy分割进行了验证.
    • 拟议的方法在图像标题的因果干预任务中显示出显著的改进.

    结论:

    • 在图像标题中,CPNet为因果干预提供了一种灵活和可适应的方法.
    • 该方法有效地改进了特征表示,以便更好地理解因果关系.
    • 在图像标题系统的因果干预方面,CPNet代表了重大进步.