Efficient inverse graphics in biological face processing.
Efficient inverse graphics in biological face processing.
复制标题
生物人脸处理中的高效逆向图形。
DOI：
10.1126/sciadv.aax5979
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发表时间：
2020
期刊：
影响因子：
13.6
通讯作者：
Tenenbaum,Josh
中科院分区：
文献类型：
--
作者：
Yildirim,Ilker;Belledonne,Mario;Freiwald,Winrich;Tenenbaum,Josh
视觉不仅检测和识别物体，而且对导致我们看到的光模式的底层场景结构进行丰富的推断。反转生成模型，或“合成分析”，提供了一种可能的解决方案，但其机械实现通常对于在线感知来说太慢，并且它们与神经回路的映射仍然不清楚。在这里，我们提出了一个神经合理的高效逆图形模型，并在人脸识别领域进行测试。该模型基于一个深度神经网络，该网络可以学习在单个快速前馈过程中反转三维人脸图形程序。它定性和定量地解释了人类的行为，包括经典的“空心脸”错觉，它直接映射到灵长类动物大脑中专门的面部处理电路。该模型比最先进的计算机视觉模型更好地拟合行为和神经数据，并提出了一种可解释的反向工程解释大脑如何将图像转换为感知。
Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.