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相关概念视频

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Perceptual Constancy01:12

Perceptual Constancy

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
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The Availability Heuristic01:08

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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学习概率的存在-缺席证据对弱监督的视听事件感知.

Junyu Gao, Mengyuan Chen, Changsheng Xu

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括

    本研究引入了一种用于弱监督视听事件感知 (WS-AVEP) 的新方法,使用概率存在缺席证据 (PPAE) 和联合模态相互学习 (JML) 改进事件本地化和分类. 这种方法提高了音频和视觉指标的性能.

    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:

    • 弱监督的视听事件感知 (WS-AVEP) 面临着由于视听数据不同步和监督较弱的挑战.
    • 现有的方法往往忽视了事件检测的音频和视觉模式的互补性质.

    研究的目的:

    • 为WS-AVEP开发一个统一的框架,有效地利用来自互补模式的存在和缺席证据.
    • 解决在弱监督下从复杂的视听数据中学习可靠证据的不确定性.

    主要方法:

    • 提出了一个概率存在缺席证据 (PPAE) 收集器,利用单模和跨模表示.
    • 引入了一种联合模式的相互学习 (JML) 过程,以适应性和动态性地校准证据.
    • 设计了一个处理可听,可视和视听事件的系统.

    主要成果:

    • 提出的方法比最先进的方法取得了显著的改进.
    • 在LLP数据集上,事件级音频和视觉指标的绝对收益分别为3.1%和4.2%.

    结论:

    • 通过利用互补的模式信息,PPAE框架和JML过程有效地增强了WS-AVEP.
    • 该方法提供了一个强大的解决方案,可以暂时定位和分类监督较弱的事件.

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