  Multi-scale context module and single-stage encoder-decoder structure are
commonly employed for semantic segmentation. The multi-scale context module
refers to the operations to aggregate feature responses from a large spatial
extent, while the single-stage encoder-decoder structure encodes the high-level
semantic information in the encoder path and recovers the boundary information
in the decoder path. In contrast, multi-stage encoder-decoder networks have
been widely used in human pose estimation and show superior performance than
their single-stage counterpart. However, few efforts have been attempted to
bring this effective design to semantic segmentation. In this work, we propose
a Semantic Prediction Guidance (SPG) module which learns to re-weight the local
features through the guidance from pixel-wise semantic prediction. We find that
by carefully re-weighting features across stages, a two-stage encoder-decoder
network coupled with our proposed SPG module can significantly outperform its
one-stage counterpart with similar parameters and computations. Finally, we
report experimental results on the semantic segmentation benchmark Cityscapes,
in which our SPGNet attains 81.1% on the test set using only 'fine'
annotations.
