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相关概念视频

Fixed Action Patterns01:06

Fixed Action Patterns

18.0K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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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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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
1.0K
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance 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...
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相关实验视频

Updated: Mar 14, 2026

Corticospinal Excitability Modulation During Action Observation
12:33

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

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ActPrompt:通过动作线索进行域内特征调整,用于视频时间接地.

Yubin Wang, Xinyang Jiang, De Cheng

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 12, 2026
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了ActPrompt,这是一种通过调整视觉语言模型 (VLMs) 来改善视频时间接地的新方法. ActPrompt有效地弥合了领域的差距,增强了视频中行动敏感模式的识别.

    相关实验视频

    Last Updated: Mar 14, 2026

    Corticospinal Excitability Modulation During Action Observation
    12:33

    Corticospinal Excitability Modulation During Action Observation

    Published on: December 31, 2013

    9.4K

    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:

    • 视频时间接地旨在在视频中找到特定的剪辑,使用预先训练的视觉语言模型 (VLM).
    • 在图像上训练的VLM直接应用于时间接地任务时,会产生域间隙,导致性能降低.
    • 这种域位转移阻碍了VLM区分动作特定模式与静态对象的能力.

    研究的目的:

    • 解决领域适应和将行动敏感信息集成到VLM中的挑战,以实现时间接地.
    • 提出一个高效的特征适应范式,减轻高适应成本.
    • 通过改进VLM特征表示来提高时间接地任务的性能.

    主要方法:

    • 在下游培训之前引入了初步的域内微调范式,以便在下游培训之前有效地适应特征.
    • 开发了Action-Cue-Injected Temporal Prompt Learning (ActPrompt),用于将动作提示注入到VLM图像编码器中. 开发了Action-Cue-Injected Temporal Prompt Learning (ActPrompt),用于注入动作提示到VLM图像编码器中.
    • 实现了上下文意识的时间快速学习,以利用行动线索和时间上下文.

    主要成果:

    • 提议的初步微调模式通过精心设计的借口任务显著提高了性能.
    • ActPrompt有效地注入动作线索,使VLM能够更好地识别动作敏感的视觉模式.
    • 广泛的实验表明,ActPrompt可以增强各种最先进的方法来完成时间接地任务.

    结论:

    • ActPrompt是一个有效的现成培训框架,用于视频时间接地.
    • 该方法成功地弥合了领域的差距,并改善了与行动相关的模式的识别.
    • ActPrompt为各种最先进的时间接地方法提供了显著的改进.