  Modern semantic segmentation frameworks usually combine low-level and
high-level features from pre-trained backbone convolutional models to boost
performance. In this paper, we first point out that a simple fusion of
low-level and high-level features could be less effective because of the gap in
semantic levels and spatial resolution. We find that introducing semantic
information into low-level features and high-resolution details into high-level
features is more effective for the later fusion. Based on this observation, we
propose a new framework, named ExFuse, to bridge the gap between low-level and
high-level features thus significantly improve the segmentation quality by
4.0\% in total. Furthermore, we evaluate our approach on the challenging PASCAL
VOC 2012 segmentation benchmark and achieve 87.9\% mean IoU, which outperforms
the previous state-of-the-art results.
