Attention-based CNNs for Image Classification: A Survey. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Zheng, Menghua; Xu, Jiayu; Shen, Yinjie; Tian, Chunwei; Li, Jian; Fei, Lunke; Zong, Ming; Liu, Xiaoyang
• Is Part Of: Journal of physics. Volume 2171:Issue 1(2022)
• Publication Date: 2022-01-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2171/1/012068
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Deep learning techniques as well as CNNs can learn power context information, they have been widely applied in image recognition. However, deep CNNs may reply on large width and large depth, which may increase computational costs. Attention mechanism fused into CNNs can address this problem. In this paper, we summary an attention mechanism acts a CNN for image classification. Firstly, the survey shows the development of CNNs for image classification. Then, we illustrate basis of CNNs and attention mechanisms for image classification. Next, we give the main architecture of CNNs with attentions, public and our collected datasets, experimental results in image classification. Finally, we point out potential research points, challenges attention-based for image classification and summary the whole paper.