  Graph-based convolutional model such as non-local block has shown to be
effective for strengthening the context modeling ability in convolutional
neural networks (CNNs). However, its pixel-wise computational overhead is
prohibitive which renders it unsuitable for high resolution imagery. In this
paper, we explore the efficiency of context graph reasoning and propose a novel
framework called Squeeze Reasoning. Instead of propagating information on the
spatial map, we first learn to squeeze the input feature into a channel-wise
global vector and perform reasoning within the single vector where the
computation cost can be significantly reduced. Specifically, we build the node
graph in the vector where each node represents an abstract semantic concept.
The refined feature within the same semantic category results to be consistent,
which is thus beneficial for downstream tasks. We show that our approach can be
modularized as an end-to-end trained block and can be easily plugged into
existing networks. {Despite its simplicity and being lightweight, the proposed
strategy allows us to establish the considerable results on different semantic
segmentation datasets and shows significant improvements with respect to strong
baselines on various other scene understanding tasks including object
detection, instance segmentation and panoptic segmentation.} Code is available
at \url{https://github.com/lxtGH/SFSegNets}.
