  In recent years, how to strike a good trade-off between accuracy and
inference speed has become the core issue for real-time semantic segmentation
applications, which plays a vital role in real-world scenarios such as
autonomous driving systems and drones. In this study, we devise a novel
lightweight network using a multi-scale context fusion (MSCFNet) scheme, which
explores an asymmetric encoder-decoder architecture to dispose this problem.
More specifically, the encoder adopts some developed efficient asymmetric
residual (EAR) modules, which are composed of factorization depth-wise
convolution and dilation convolution. Meanwhile, instead of complicated
computation, simple deconvolution is applied in the decoder to further reduce
the amount of parameters while still maintaining high segmentation accuracy.
Also, MSCFNet has branches with efficient attention modules from different
stages of the network to well capture multi-scale contextual information. Then
we combine them before the final classification to enhance the expression of
the features and improve the segmentation efficiency. Comprehensive experiments
on challenging datasets have demonstrated that the proposed MSCFNet, which
contains only 1.15M parameters, achieves 71.9\% Mean IoU on the Cityscapes
testing dataset and can run at over 50 FPS on a single Titan XP GPU
configuration.
