  Improving the efficiency of state-of-the-art methods in semantic segmentation
requires overcoming the increasing computational cost as well as issues such as
fusing semantic information from global and local contexts. Based on the recent
success and problems that convolutional neural networks (CNNs) encounter in
semantic segmentation, this research proposes an encoder-decoder architecture
with a unique efficient residual network, Efficient-ResNet. Attention-boosting
gates (AbGs) and attention-boosting modules (AbMs) are deployed by aiming to
fuse the equivariant and feature-based semantic information with the equivalent
sizes of the output of global context of the efficient residual network in the
encoder. Respectively, the decoder network is developed with the additional
attention-fusion networks (AfNs) inspired by AbM. AfNs are designed to improve
the efficiency in the one-to-one conversion of the semantic information by
deploying additional convolution layers in the decoder part. Our network is
tested on the challenging CamVid and Cityscapes datasets, and the proposed
methods reveal significant improvements on the residual networks. To the best
of our knowledge, the developed network, SERNet-Former, achieves
state-of-the-art results (84.62 % mean IoU) on CamVid dataset and challenging
results (87.35 % mean IoU) on Cityscapes validation dataset.
