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相关概念视频

Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
841
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
431
Stereotype Content Model02:16

Stereotype Content Model

15.3K
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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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
447

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相关实验视频

Updated: Jan 17, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

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重新思考通用零射击学习:一个合成的每实例属性视角.

Chenwei Tang, Ying Wang, Wei Xie

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 15, 2025
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    概括
    此摘要是机器生成的。

    每个实例的属性合成 (PIAS) 产生多样化的语义表示,用于一般化零射击学习 (GZSL),无需手动注释. 这种方法通过弥合视觉语义空间中的语义差距来增强对未见类的概括性.

    相关实验视频

    Last Updated: Jan 17, 2026

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 一般化零射击学习 (GZSL) 旨在改善对未见类的模型概括.
    • 现有的GZSL方法因依赖每个类属性而扎于语义差距和域转移.
    • 实例级属性提供了一个解决方案,但需要昂贵的手动注释.

    研究的目的:

    • 提出一种新的方法,即每实例属性合成 (PIAS),用于生成多样化的语义表示.
    • 通过消除手动属性注释的需要,解决传统GZSL方法的局限性.
    • 提高视觉和语义表示的可辨别性,以提高GZSL的性能.

    主要方法:

    • 使用视觉转换器 (ViT) 来进行视觉特征提取和每实例属性生成.
    • 使用类平均图像的生成属性定义类点,并在语义空间中对它们进行校准.
    • 通过在注释和合成的属性和特征之间对准拓结构来提高属性多样性.

    主要成果:

    • 在ZSL和GZSL设置中,PIAS显著优于AWA2,CUB和SUN数据集的最新方法.
    • 证明了拟议方法的改进的概括能力.
    • 成功地将PIAS应用于基于属性的零拍摄图像检索任务.

    结论:

    • PIAS提供了一种有效和高效的解决方案,用于在GZSL中生成各种各样的每个实例属性.
    • 该方法成功地弥合了语义差距,并减轻了域移动问题.
    • PIAS显示了对现实世界应用的巨大潜力,需要对未见的类进行强有力的概括.