  Deep networks for image classification often rely more on texture information
than object shape. While efforts have been made to make deep-models
shape-aware, it is often difficult to make such models simple, interpretable,
or rooted in known mathematical definitions of shape. This paper presents a
deep-learning model inspired by geometric moments, a classically well
understood approach to measure shape-related properties. The proposed method
consists of a trainable network for generating coordinate bases and affine
parameters for making the features geometrically invariant yet in a
task-specific manner. The proposed model improves the final feature's
interpretation. We demonstrate the effectiveness of our method on standard
image classification datasets. The proposed model achieves higher
classification performance compared to the baseline and standard ResNet models
while substantially improving interpretability.
