  One of the practical choices for making a lightweight semantic segmentation
model is to combine a depth-wise separable convolution with a dilated
convolution. However, the simple combination of these two methods results in an
over-simplified operation which causes severe performance degradation due to
loss of information contained in the feature map. To resolve this problem, we
propose a new block called Concentrated-Comprehensive Convolution (C3) which
applies the asymmetric convolutions before the depth-wise separable dilated
convolution to compensate for the information loss due to dilated convolution.
The C3 block consists of a concentration stage and a comprehensive convolution
stage. The first stage uses two depth-wise asymmetric convolutions for
compressed information from the neighboring pixels to alleviate the information
loss. The second stage increases the receptive field by using a depth-wise
separable dilated convolution from the feature map of the first stage. We
applied the C3 block to various segmentation frameworks (ESPNet, DRN, ERFNet,
ENet) for proving the beneficial properties of our proposed method.
Experimental results show that the proposed method preserves the original
accuracies on Cityscapes dataset while reducing the complexity. Furthermore, we
modified ESPNet to achieve about 2% better performance while reducing the
number of parameters by half and the number of FLOPs by 35% compared with the
original ESPNet. Finally, experiments on ImageNet classification task show that
C3 block can successfully replace dilated convolutions.
