HAM: Hybrid attention module in deep convolutional neural networks for image classification. (September 2022)

• Record Type: Journal Article
• Authors: Li, Guoqiang; Fang, Qi; Zha, Linlin; Gao, Xin; Zheng, Nenggan
• Is Part Of: Pattern recognition. Volume 129(2022)
• Publication Date: 2022-09
• Subjects: Hybrid attention module -- Channel attention map -- Spatial feature descriptor -- HAM-integrated networks; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108785
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Proposing an attention module: Hybrid Attention Module (HAM). HAM can be embedded into any state-of-the-art CNN architectures. HAM improve networks performance without significantly increasing parameters. Compared with other state-of-the-art attention modules, HAM achieve better performance on the standard datasets. On STL-10 datasets, HAM can further reduce the negative impact of less data on the performance as networks go deeper. Abstract: Recently, many researches have demonstrated that the attention mechanism has great potential in improving the performance of deep convolutional neural networks (CNNs). However, the existing methods either ignore the importance of using channel attention and spatial attention mechanisms simultaneously or bring much additional model complexity. In order to achieve a balance between performance and model complexity, we propose the Hybrid Attention Module (HAM), a really lightweight yet efficient attention module. Given an intermediate feature map as the input feature, HAM firstly produces one channel attention map and one channel refined feature through the channel submodule, and then based on the channel attention map, the spatial submodule divides the channel refined feature into two groups along the channel axis to generate a pair of spatial attention descriptors. By applying saptial attention descriptors, the spatial submodule generates the final refined feature which can adaptively emphasize the important regions. Besides, HAM is a simple and general module, it can be embedded into various mainstream deep CNN architectures seamlessly and can be trained with base CNNs in the end-to-end way. We evaluate HAM through abundant of experiments on CIFAR-10, CIFAR-100 and STL-10 datasets. The experimental results show that HAM-integrated networks achieve accuracy improvements and further reduce the negative impact of less training data on deeper networks performance than its counterparts, which proves the effectiveness of HAM.