  The Non-Local Network (NLNet) presents a pioneering approach for capturing
long-range dependencies within an image, 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 the non-local network are
almost the same for different query positions. 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 replace the one-layer transformation function of the
non-local block by a two-layer bottleneck, which further reduces the parameter
number considerably. The resulting network element, called the global context
(GC) block, effectively models global context in a lightweight manner, allowing
it to be applied at multiple layers of a backbone network to form a global
context network (GCNet). Experiments show that GCNet generally outperforms
NLNet on major benchmarks for various recognition tasks. The code and network
configurations are available at https://github.com/xvjiarui/GCNet.
