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Updated: Jul 16, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Published on: November 2, 2012

人类形状表示不是学习对象分类的新兴属性

Gaurav Malhotra¹, Marin Dujmović¹, John Hummel²

¹School of Psychological Sciences, University of Bristol.

Journal of experimental psychology. General

|September 11, 2023

概括

人类对对象部分关系具有独特的敏感性,与人工神经网络不同. 这种敏感性来自于推断对象属性,而不仅仅是图像分类学习.

科学领域:

认知科学认知科学
计算机视觉计算机视觉
心理学心理学心理学

背景情况:

人类对物体部分之间的关系表现出强烈的敏感性.
这种人类敏感性的根本原因,特别是对象识别,仍然在很大程度上无法解释.
一个突出的假设表明,关系特征对于对象分类至关重要,并通过学习来发展.

研究的目的:

调查监督卷积神经网络 (CNN) 是否对对象的关系变化产生类似人类的敏感性.
为了比较CNN和人类在暴露于对象变形时的内部表示和行为反应.
确定操纵学习环境以强调关系特征是否会提高CNN对这些特征的敏感性.

主要方法:

分析在大型对象数据集上训练有素的受监督的CNN的内部表示.
将人类对物体变形的行为反应与CNN对相同变化的敏感性进行比较.
在修改的学习条件下测试CNN性能和灵敏度,在这种条件下,关系信息被单独诊断.

主要成果:

CNNs没有复制人类对对象部分关系变化的敏感性.
人类行为的最佳预测是关系变化的数量,而CNN对所有类型的变化都表现出一致的敏感性.
即使在关系是唯一诊断的环境中接受培训时,CNN也没有对关系变化产生一般的敏感性.

结论:

学习对象的分类对人工神经网络来说是不够的,以开发类似人类的形状表示.
人类对关系变化的敏感性似乎源于从视网膜图像中推断远距离物体的属性.
这种推断过程在人类对象表示和在人工神经网络中优化图像分类的对象表示之间产生了定性差异.

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