Attention graph: Learning effective visual features for large-scale image classification. (January 2022)

• Record Type: Journal Article
• Authors: Cui, Xuelian; Zhang, Zhanjie; Zhang, Tao; Yang, Zhuoqun; Yang, Jie
• Is Part Of: Journal of algorithms & computational technology. Volume 16(2022)
• Publication Date: 2022-01
• Subjects: Image classification -- channel attention module -- spatial attention module -- deep learning -- residual network; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/17483026211065375
• Languages: English
• ISSNs: 1748-3018

Abstract

In recent years, the research of deep learning has received extensive attention, and many breakthroughs have been made in various fields. On this basis, a neural network with the attention mechanism has become a research hotspot. In this paper, we try to solve the image classification task by implementing channel and spatial attention mechanism which improve the expression ability of neural network model. Different from previous studies, we propose an attention module consisting of channel attention module (CAM) and spatial attention module (SAM). The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. Besides, this module is lightweight and can be easily integrated into image classification algorithms. In the experiment, we combine the deep residual network model with the attention module and the experimental results show that the proposed method brings higher image classification accuracy. The channel attention module adds weight to the signals on different convolution channels to represent the correlation. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Through a large number of comparative experiments, our proposed model achieved outstanding performance.