  Current semantic segmentation models only exploit first-order statistics,
while rarely exploring high-order statistics. However, common first-order
statistics are insufficient to support a solid unanimous representation. In
this paper, we propose High-Order Paired-ASPP Network to exploit high-order
statistics from various feature levels. The network first introduces a
High-Order Representation module to extract the contextual high-order
information from all stages of the backbone. They can provide more semantic
clues and discriminative information than the first-order ones. Besides, a
Paired-ASPP module is proposed to embed high-order statistics of the early
stages into the last stage. It can further preserve the boundary-related and
spatial context in the low-level features for final prediction. Our experiments
show that the high-order statistics significantly boost the performance on
confusing objects. Our method achieves competitive performance without bells
and whistles on three benchmarks, i.e, Cityscapes, ADE20K and Pascal-Context
with the mIoU of 81.6%, 45.3% and 52.9%.
