A Visual Attention based Convolutional Neural Network for Image Classification
Chen Yaran1; Zhao Dongbin1; Lv Le1; Li Chengdong2
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.
DOI10.1109/WCICA.2016.7578651
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文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14478
专题复杂系统管理与控制国家重点实验室_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 (
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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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