Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 45, Issue 6, Pages 6881-6895Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2020.3047209
Keywords
Convolution; Task analysis; Context modeling; Computer architecture; Visualization; Computational modeling; Feature extraction; deep network; self-attention model; global context; object detection
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The non-local network (NLNet) proposes a pioneering approach to capture long-range dependencies within an image by aggregating query-specific global context to query positions. However, empirical analysis reveals that the global contexts modeled by NLNet are almost the same for different query positions. In this paper, a simplified network called global context (GC) block is proposed based on a query-independent formulation, which maintains the accuracy of NLNet with significantly reduced computation.
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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