MCFNet: Multi-scale Covariance Feature Fusion Network for Real-time
  Semantic Segmentation
Xiaojie Fang Xingguo Song Xiangyin Meng Xu Fang Sheng Jin
The low-level spatial detail information and high-level semantic abstract
information are both essential to the semantic segmentation task. The features
extracted by the deep network can obtain rich semantic information, while a lot
of spatial information is lost. However, how to recover spatial detail
information effectively and fuse it with high-level semantics has not been well
addressed so far. In this paper, we propose a new architecture based on
Bilateral Segmentation Network (BiseNet) called Multi-scale Covariance Feature
Fusion Network (MCFNet). Specifically, this network introduces a new feature
refinement module and a new feature fusion module. Furthermore, a gating unit
named L-Gate is proposed to filter out invalid information and fuse multi-scale
features. We evaluate our proposed model on Cityscapes, CamVid datasets and
compare it with the state-of-the-art methods. Extensive experiments show that
our method achieves competitive success. On Cityscapes, we achieve 75.5% mIOU
with a speed of 151.3 FPS.