  The Non-Local Network (NLNet) presents a pioneering approach for capturing
long-range dependencies, via aggregating query-specific global context to each
query position. However, through a rigorous empirical analysis, we have found
that the global contexts modeled by non-local network are almost the same for
different query positions within an image. In this paper, we take advantage of
this finding to create a simplified network based on a query-independent
formulation, which maintains the accuracy of NLNet but with significantly less
computation. We further observe that this simplified design shares similar
structure with Squeeze-Excitation Network (SENet). Hence we unify them into a
three-step general framework for global context modeling. Within the general
framework, we design a better instantiation, called the global context (GC)
block, which is lightweight and can effectively model the global context. The
lightweight property allows us to apply it for multiple layers in a backbone
network to construct a global context network (GCNet), which generally
outperforms both simplified NLNet and SENet on major benchmarks for various
recognition tasks. The code and configurations are released at
https://github.com/xvjiarui/GCNet.
