  In this work, we evaluate the use of superpixel pooling layers in deep
network architectures for semantic segmentation. Superpixel pooling is a
flexible and efficient replacement for other pooling strategies that
incorporates spatial prior information. We propose a simple and efficient
GPU-implementation of the layer and explore several designs for the integration
of the layer into existing network architectures. We provide experimental
results on the IBSR and Cityscapes dataset, demonstrating that superpixel
pooling can be leveraged to consistently increase network accuracy with minimal
computational overhead. Source code is available at
https://github.com/bermanmaxim/superpixPool
