  Global context information is vital in visual understanding problems,
especially in pixel-level semantic segmentation. The mainstream methods adopt
the self-attention mechanism to model global context information. However,
pixels belonging to different classes usually have weak feature correlation.
Modeling the global pixel-level correlation matrix indiscriminately is
extremely redundant in the self-attention mechanism. In order to solve the
above problem, we propose a hierarchical context network to differentially
model homogeneous pixels with strong correlations and heterogeneous pixels with
weak correlations. Specifically, we first propose a multi-scale guided
pre-segmentation module to divide the entire feature map into different
classed-based homogeneous regions. Within each homogeneous region, we design
the pixel context module to capture pixel-level correlations. Subsequently,
different from the self-attention mechanism that still models weak
heterogeneous correlations in a dense pixel-level manner, the region context
module is proposed to model sparse region-level dependencies using a unified
representation of each region. Through aggregating fine-grained pixel context
features and coarse-grained region context features, our proposed network can
not only hierarchically model global context information but also harvest
multi-granularity representations to more robustly identify multi-scale
objects. We evaluate our approach on Cityscapes and the ISPRS Vaihingen
dataset. Without Bells or Whistles, our approach realizes a mean IoU of 82.8%
and overall accuracy of 91.4% on Cityscapes and ISPRS Vaihingen test set,
achieving state-of-the-art results.
