Learning a Hierarchical Global Attention for Image Classification

Kerang Cao, Jingyu Gao, Kwang-nam Choi and Lini Duan
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Kerang Cao: Shenyang University of Chemical Technology, Shenyang 110000, China
Jingyu Gao: Shenyang University of Chemical Technology, Shenyang 110000, China
Kwang-nam Choi: NTIS Center, Korea Institute of Science and Technology Information, Seoul 02792, Korea
Lini Duan: Shenyang University of Chemical Technology, Shenyang 110000, China

Future Internet, 2020, vol. 12, issue 11, 1-11

Abstract: To classify the image material on the internet, the deep learning methodology, especially deep neural network, is the most optimal and costliest method of all computer vision methods. Convolutional neural networks (CNNs) learn a comprehensive feature representation by exploiting local information with a fixed receptive field, demonstrating distinguished capacities on image classification. Recent works concentrate on efficient feature exploration, which neglect the global information for holistic consideration. There is large effort to reduce the computational costs of deep neural networks. Here, we provide a hierarchical global attention mechanism that improve the network representation with restricted increase of computation complexity. Different from nonlocal-based methods, the hierarchical global attention mechanism requires no matrix multiplication and can be flexibly applied in various modern network designs. Experimental results demonstrate that proposed hierarchical global attention mechanism can conspicuously improve the image classification precision—a reduction of 7.94% and 16.63% percent in Top 1 and Top 5 errors separately—with little increase of computation complexity (6.23%) in comparison to competing approaches.

Keywords: image classification; attention mechanism; convolutional neural network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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