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相关概念视频

Associative Learning01:27

Associative Learning

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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...
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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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The Representativeness Heuristic02:13

The Representativeness Heuristic

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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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Sensory Modalities01:15

Sensory Modalities

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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
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Nonconscious Mimicry01:13

Nonconscious Mimicry

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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注意差距:学习模态-无模态表示与交叉模态UNet.

Xin Niu, Enyi Li, Jinchao Liu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 8, 2024
    PubMed
    概括

    本研究介绍了MarrNet,这是一种用于跨模式识别的新方法,它使用一个紧的编码器解码器神经模块 (cmUNet) 学习模式不可知表示. 在各种具有挑战性的任务中,MarrNet 实现了卓越的性能和对阻塞的稳定性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 交叉模式识别将来自不同来源 (例如图像,光谱) 的数据用于科学和安全任务.
    • 现有的方法在信息丢失或依赖难以传输的明确方式上扎.
    • 需要一个强有力的方法来有效地处理模式差距.

    研究的目的:

    • 开发一种新的神经网络模块 (cmUNet),用于学习模态-不可知表示.
    • 提出MarrNet,一个集成cmUNet的系统,用于优越的跨模式匹配.
    • 提高跨模式识别中对遮和伪装的稳定性.

    主要方法:

    • 提出了一个紧的编码器-解码器神经模块 (cmUNet) 用于模态不可知的表示学习.
    • 雇佣跨模式转型和在模式内重建与对抗性/感知性损失.
    • 将cmUNet集成到MarrNet中,用于跨模式匹配任务,输出相似性得分.

    主要成果:

    • 马尔网在拉曼红外频谱匹配,人重新识别和异质人脸识别方面表现出卓越的表现.
    • 与最先进的技术相比,该方法实现了超过10%的改进.
    • 马尔内特表现出极好的抗阻塞和伪装强度,表明有效的模式弥合差距.

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    结论:

    • 拟议的cmUNet是各种跨模式应用的多功能构建块.
    • 马尔Net为跨模式识别挑战提供了强大而高性能的解决方案.
    • 对遮的坚固性是成功弥合模式差距的关键指标.