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.

翻译：形状学习，即利用形状信息的能力，当目标对象具有特定形状时，可能是卷积神经网络（CNN）的一个理想特性。尽管已有部分研究关注这一课题，但尚缺乏系统性研究来明确判断CNN是否以及何种情况下会学习形状。本文针对形状尤为重要的分割网络场景开展了此类研究。我们定义了形状概念，并提出了一种新的行为度量指标来量化CNN利用形状信息的程度。随后通过一系列合成数据与真实数据实验，逐步揭示了CNN学习形状的条件以及促进该行为的可行方法。主要结论如下：（i）在典型设置下CNN并不学习形状，而是依赖其他可用特征来识别目标对象；（ii）CNN能够学习形状，但仅当形状是识别对象的唯一可用特征时；（iii）相对目标对象尺寸足够大的感受野是形状学习的必要条件；（iv）有限的数据增强方法可促进形状学习；（v）在遇到分布外数据时，学习形状确实具有实用价值。