Global Context Networks

• URL: http://arxiv.org/abs/2012.13375v1
• Date: Thu, 24 Dec 2020 18:02:57 GMT
• Title: Global Context Networks
• Abstract summary: The Non-Local Network (NLNet) is a pioneering approach for capturing long-range dependencies within an image. We show that the global contexts modeled by the non-local network are almost the same for different query positions. We take advantage of this finding to create a simplified network based on a query-independent formulation.
• Score: 52.597260561253655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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