Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation
Pattern Recognition(2023)
Abstract
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.
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Key words
Segmentation,Feature measurement,Machine learning,Computer vision
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