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Updated: Jul 5, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

在视觉概念学习中的组成多样性

Yanli Zhou¹, Reuben Feinman², Brenden M Lake³

¹Center for Data Science, New York University, United States of America.

|January 15, 2024

概括

人类通过构成性在视觉概念学习中表现出色,与计算机视觉模型不同. 这项研究模拟了人类在分类和生成新奇物体方面的能力,揭示了对构成概括和人类假设的洞察力.

科学领域:

认知科学认知科学
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

人类通过结合熟悉的部分 (组合性) 有效地学习新概念.
与人类相比,计算机视觉模型通常需要大量的数据,并且在概括概念方面具有有限的灵活性.
了解组成概括是开发更类似人类的人工智能的关键.

研究的目的:

研究人类在视觉构成中的能力,以对新型对象进行分类和生成.
开发能够复制和解释人类组成概括的计算模型.
将贝叶斯程序诱导和神经符号程序诱导模型与人类行为的性能进行比较.

主要方法:

人类分类和产生具有关系结构的"外星人形象"的实验研究.
开发贝叶斯程序感应模型,用于推断可视图形的生成程序.
开发一种神经符号程序诱导模型,其中包含神经网络模块用于残余结构.

主要成果:

人类和贝叶斯程序感应模型都在一些射击分类任务中展示了组成概括.
该模型提供了对人类数据的强有力的解释,揭示了关于旋转和部件附着等不变因素的假设.
在几次拍摄的生成中,人类和模型都创造了新的例子,尽管人类表现出了诸如集完成和部分重新配置等额外的行为.

结论:

人类和计算模型可以在视觉分类和生成中表现出组合行为.
神经符号程序诱导模型可以捕捉到超越传统贝叶斯模型的额外人类行为模式.
这些发现促进了对人类概念学习的理解,并为开发更灵活的人工智能系统提供了信息.

关键词:

贝叶斯的推理贝叶斯的推理复合性复合性是指复合性.概念学习学习概念学习有几次射击学习学习.神经符号模型的神经符号模型视觉学习视觉学习

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