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2020

Global Context Networks

Cao, Yue, Xu, Jiarui, Lin, Stephen et al.

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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.

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