经过训练以估计反射率和照度的深度神经网络,与人类观察者不同,实现了不同程度的亮度常数
Alban Flachot1,2, Jaykishan Patel1,3, Thomas S A Wallis4,5,6
1Department of Psychology and Centre for Vision Research, York University, Toronto, Ontario, Canada.
Journal of vision
|February 18, 2026
概括
卷积神经网络 (CNN) 通过使用全球策略,与人类相比,表现出更高的轻度常数. 然而,CNN和人类依赖于不同的视觉线索,CNN利用染图像中的噪音工件.
科学领域:
- 计算神经科学是一种计算神经科学.
- 计算机视觉 计算机视觉 计算机视觉
- 人类的视觉感知 人类的视觉感知
背景情况:
- 亮度恒定对于在不同的照明下感知表面反射率至关重要.
- 开发人类轻度常数的图像可计算模型仍然是一个挑战.
- 卷积神经网络 (CNN) 在反射率估计方面表现有前途,但它们的机制和与人类视觉的关系尚不清楚.
研究的目的:
- 为了研究CNN如何实现轻度常数.
- 将CNN的表现和提示利用与人类观察者进行比较.
- 为了确定CNN是否适合人类视觉处理轻度的模型.
主要方法:
- 训练了一个CNN在虚拟环境中估计反射和照明.
- 在不同的提示条件下评估CNN和人类在轻度匹配任务中的表现.
- 操纵的线索,如局部对比度,阴影,影子和上下文信息.
主要成果:
- 与人类相比,CNN实现了更高的轻度常数,整体表现优于它们.
- 线索删除对CNN和人类的影响不同:人类对局部对比很敏感,而CNN则依赖于遮阳和阴影.
- 美国有线电视新闻网学会了利用与照明度相关的射线跟踪噪声工件.
结论:
- CNNs采用有效的全球策略来估计轻度,在研究场景中超越了人类的表现.
- 对于轻度常数,CNN和人类使用不同的提示策略.
- 这些发现对在视觉研究中使用光线跟踪图像有意义,并突出了人造和人类视觉系统之间的差异.
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