Shape learning, or the ability to leverage shape information, could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes. While some research on the topic is emerging, there is no systematic study to conclusively determine whether and under what circumstances CNNs learn shape. Here, we present such a study in the context of segmentation networks where shapes are particularly important. We define shape and propose a new behavioral metric to measure the extent to which a CNN utilizes shape information. We then execute a set of experiments with synthetic and real-world data to progressively uncover under which circumstances CNNs learn shape and what can be done to encourage such behavior. We conclude that (i) CNNs do not learn shape in typical settings but rather rely on other features available to identify the objects of interest, (ii) CNNs can learn shape, but only if the shape is the only feature available to identify the object, (iii) sufficiently large receptive field size relative to the size of target objects is necessary for shape learning; (iv) a limited set of augmentations can encourage shape learning; (v) learning shape is indeed useful in the presence of out-of-distribution data.

通过一系列实验，发现在典型的设置下CNN并不会学习物体的形状，而是依赖其他特征，但只有在形状是识别物体的唯一特征时，CNN才能学习形状。此外，适当的感受野大小相对于目标物体的大小和有限的扩充能够鼓励形状学习，并且在存在超出分布的数据时，学习形状是确实有用的。

卷积神经网络在语义分割任务中很少学习形状信息