对象识别强度的发展轨迹:儿童就像小成年人,但与大深度神经网络不同
Lukas S Huber1,2,3,4, Robert Geirhos2,5,6, Felix A Wichmann2,7,8
1Department of Psychology, University of Bern, Bern, Switzerland.
Journal of vision
|July 6, 2023
概括
儿童在发育早期表现出了显著的对象识别强度,以对抗图像扭曲,表现优于深度神经网络 (DNN) 并需要更少的数据. 这表明人类的健壮性不仅仅来自广泛的视觉体验.
科学领域:
- 发展心理学 发展心理学
- 计算机视觉 计算机视觉
- 认知科学 认知科学
背景情况:
- 深度神经网络 (DNN) 在清晰图像的物体识别方面表现出色,但与成年人不同,与扭曲作斗争.
- 最近的DNN在扭曲强度方面的进步依赖于大规模的数据集,引发了关于人类视觉发育的问题.
- 人类对象识别的稳定性对于现实世界的理解至关重要,但它的发展起源仍然不清楚.
研究的目的:
- 研究儿童对象识别强度的发展轨迹.
- 为了比较儿童,成人和深度神经网络 (DNN) 的扭曲强度.
- 探索数据曝光和视觉线索 (形状与纹理) 在开发强度方面的作用.
主要方法:
- 在146名儿童 (4-15岁),成年人和各种DNN中对扭曲图像进行对象识别性能比较.
- 估计了儿童一生中视觉数据暴露的数量.
- 在对象识别过程中分析了对形状和纹理线索的依赖.
主要成果:
- 4至6岁的儿童表现出显著的扭曲强度,超过了ImageNet训练的DNN.
- 与DNN相比,儿童的强度在显著较少的数据暴露下实现.
- 儿童和成年人优先考虑形状提示,而DNN显示出不同的提示依赖.
结论:
- 人类对象识别强度在发育的早期出现,不仅仅是积累的视觉经验的产物.
- 儿童的强度比目前的DNN策略更有效地处理数据.
- 人类的发育强度似乎利用了与最先进的DNN使用的不同机制.
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