  In this paper, we propose an effective method for fast and accurate scene
parsing called Bidirectional Alignment Network (BiAlignNet). Previously, one
representative work BiSeNet~\cite{bisenet} uses two different paths (Context
Path and Spatial Path) to achieve balanced learning of semantics and details,
respectively. However, the relationship between the two paths is not well
explored. We argue that both paths can benefit each other in a complementary
way. Motivated by this, we propose a novel network by aligning two-path
information into each other through a learned flow field. To avoid the noise
and semantic gaps, we introduce a Gated Flow Alignment Module to align both
features in a bidirectional way. Moreover, to make the Spatial Path learn more
detailed information, we present an edge-guided hard pixel mining loss to
supervise the aligned learning process. Our method achieves 80.1\% and 78.5\%
mIoU in validation and test set of Cityscapes while running at 30 FPS with full
resolution inputs. Code and models will be available at
\url{https://github.com/jojacola/BiAlignNet}.
