  Feature fusion modules from encoder and self-attention module have been
adopted in semantic segmentation. However, the computation of these modules is
costly and has operational limitations in real-time environments. In addition,
segmentation performance is limited in autonomous driving environments with a
lot of contextual information perpendicular to the road surface, such as
people, buildings, and general objects. In this paper, we propose an efficient
feature fusion method, Feature Fusion with Different Norms (FFDN) that utilizes
rich global context of multi-level scale and vertical pooling module before
self-attention that preserves most contextual information while reducing the
complexity of global context encoding in the vertical direction. By doing this,
we could handle the properties of representation in global space and reduce
additional computational cost. In addition, we analyze low performance in
challenging cases including small and vertically featured objects. We achieve
the mean Interaction of-union(mIoU) of 73.1 and the Frame Per Second(FPS) of
191, which are comparable results with state-of-the-arts on Cityscapes test
datasets.
