A Visual Attention based Convolutional Neural Network for Image Classification

doi:10.1109/WCICA.2016.7578651

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 深度强化学习

	A Visual Attention based Convolutional Neural Network for Image Classification
	Chen Yaran1 ; Zhao Dongbin1 ; Lv Le1 ; Li Chengdong 2
	2016-09
会议名称	The 2016 World Congress on Intelligent Control and Automation
会议日期	12-15 June 2016
会议地点	Guilin, China
摘要	This paper presents a visual attention based convolutional neural network (CNN) to solve the image classification problem in the real complex world scene. The presented method can simulate the process of recognizing objects and find the area of interest which is related with the task. Compared with the CNN method in image classification, the model is proficient in fine-grained classification problem and has a better robustness due to its mechanism of multi-glance and visual attention. We evaluate the model on vehicle dataset, where its performance exceeds CNN baseline on image classification.
DOI	10.1109/WCICA.2016.7578651
引用统计
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14478
专题	多模态人工智能系统全国重点实验室_深度强化学习
作者单位	1.he State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.he School of Information and Electrical Engineer- ing, Shandong Jianzhu University, Jinan 250101, China (
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Chen Yaran,Zhao Dongbin,Lv Le,et al. A Visual Attention based Convolutional Neural Network for Image Classification[C],2016.

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