Deep convolutional neural networks (DCNNs) show impressive similarities to the human visual system. Recent research, however, suggests that DCNNs have limitations in recognizing objects by their shape. We tested the hypothesis that DCNNs are sensitive to an object's local contour features but have no access to global shape information that predominates human object recognition. We employed transfer learning to assess local and global shape processing in trained networks. In Experiment 1, we used restricted and unrestricted transfer learning to retrain AlexNet, VGG-19, and ResNet-50 to classify circles and squares. We then probed these networks with stimuli with conflicting global shape and local contour information. We presented networks with overall square shapes comprised of curved elements and circles comprised of corner elements. Networks classified the test stimuli by local contour features rather than global shapes. In Experiment 2, we changed the training data to include circles and squares comprised of different elements so that the local contour features of the object were uninformative. This considerably increased the network's tendency to produce global shape responses, but deeper analyses in Experiment 3 revealed the network still showed no sensitivity to the spatial configuration of local elements. These findings demonstrate that DCNNs' performance is an inversion of human performance with respect to global and local shape processing. Whereas abstract relations of elements predominate in human perception of shape, DCNNs appear to extract only local contour fragments, with no representation of how they spatially relate to each other to form global shapes.

中文翻译：

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深度卷积神经网络在目标分类中的局部特征和全局形状信息。

深度卷积神经网络（DCNN）与人类视觉系统具有惊人的相似性。但是，最近的研究表明，DCNN在通过其形状识别对象方面存在局限性。我们测试了以下假设：DCNN对对象的局部轮廓特征敏感，但无法访问占主导地位的全局形状信息。我们使用转移学习来评估经过培训的网络中的局部和全局形状处理。在实验1中，我们使用了受限和非受限转移学习来重新训练AlexNet，VGG-19和ResNet-50，以对圆形和正方形进行分类。然后，我们使用具有冲突的全局形状和局部轮廓信息的刺激来探索这些网络。我们介绍了具有由弯曲元素组成的整体正方形和由角元素组成的圆形的网络。网络通过局部轮廓特征而不是整体形状对测试刺激进行分类。在实验2中，我们将训练数据更改为包括由不同元素组成的圆形和正方形，以使对象的局部轮廓特征不具有信息性。这大大增加了网络产生整体形状响应的趋势，但是在实验3中更深入的分析表明，网络仍然对局部元素的空间配置不敏感。这些发现表明，就整体和局部形状处理而言，DCNN的性能是人类性能的倒置。元素的抽象关系在人类对形状的感知中占主导地位，而DCNN似乎仅提取局部轮廓片段，而没有表示它们在空间上如何相互联系以形成整体形状。