  It has been widely proven that modelling long-range dependencies in fully
convolutional networks (FCNs) via global aggregation modules is critical for
complex scene understanding tasks such as semantic segmentation and object
detection. However, global aggregation is often dominated by features of large
patterns and tends to oversmooth regions that contain small patterns (e.g.,
boundaries and small objects). To resolve this problem, we propose to first use
\emph{Global Aggregation} and then \emph{Local Distribution}, which is called
GALD, where long-range dependencies are more confidently used inside large
pattern regions and vice versa. The size of each pattern at each position is
estimated in the network as a per-channel mask map. GALD is end-to-end
trainable and can be easily plugged into existing FCNs with various global
aggregation modules for a wide range of vision tasks, and consistently improves
the performance of state-of-the-art object detection and instance segmentation
approaches. In particular, GALD used in semantic segmentation achieves new
state-of-the-art performance on Cityscapes test set with mIoU 83.3\%. Code is
available at: \url{https://github.com/lxtGH/GALD-Net}
