  Modelling long-range contextual relationships is critical for pixel-wise
prediction tasks such as semantic segmentation. However, convolutional neural
networks (CNNs) are inherently limited to model such dependencies due to the
naive structure in its building modules (\eg, local convolution kernel). While
recent global aggregation methods are beneficial for long-range structure
information modelling, they would oversmooth and bring noise to the regions
containing fine details (\eg,~boundaries and small objects), which are very
much cared for the semantic segmentation task. To alleviate this problem, we
propose to explore the local context for making the aggregated long-range
relationship being distributed more accurately in local regions. In particular,
we design a novel local distribution module which models the affinity map
between global and local relationship for each pixel adaptively. Integrating
existing global aggregation modules, we show that our approach can be
modularized as an end-to-end trainable block and easily plugged into existing
semantic segmentation networks, giving rise to the \emph{GALD} networks.
Despite its simplicity and versatility, our approach allows us to build new
state of the art on major semantic segmentation benchmarks including
Cityscapes, ADE20K, Pascal Context, Camvid and COCO-stuff. Code and trained
models are released at \url{https://github.com/lxtGH/GALD-DGCNet} to foster
further research.
