  This paper introduces an efficient patch-based computational module, coined
Entropy-based Patch Encoder (EPE) module, for resource-constrained semantic
segmentation. The EPE module consists of three lightweight fully-convolutional
encoders, each extracting features from image patches with a different amount
of entropy. Patches with high entropy are being processed by the encoder with
the largest number of parameters, patches with moderate entropy are processed
by the encoder with a moderate number of parameters, and patches with low
entropy are processed by the smallest encoder. The intuition behind the module
is the following: as patches with high entropy contain more information, they
need an encoder with more parameters, unlike low entropy patches, which can be
processed using a small encoder. Consequently, processing part of the patches
via the smaller encoder can significantly reduce the computational cost of the
module. Experiments show that EPE can boost the performance of existing
real-time semantic segmentation models with a slight increase in the
computational cost. Specifically, EPE increases the mIOU performance of DFANet
A by 0.9% with only 1.2% increase in the number of parameters and the mIOU
performance of EDANet by 1% with 10% increase of the model parameters.
