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Proceedings Paper

Research for image caption based on global attention mechanism
Author(s): Tong Wu; Tao Ku; Hao Zhang
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Paper Abstract

Convolution Neural Networks (CNN) and Recurrent Neural Networks (RNN), which are the main research methods of image caption, have developed rapidly. Nevertheless lacking of global consciousness in image caption has not been completely solved. Separation from the bottom-up visual attention mechanism and the top-down visual attention mechanism has been widely used in image description and visual question and answers. In this article, we put forward image description based on a global attention mechanism research methods. The global attention prior channel is added to the infrastructure to extract the global information features while learning the local features. The attention to the object and other outstanding level of image region is calculated, and the global image features are enhanced.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114272U (31 January 2020);
Show Author Affiliations
Tong Wu, Liaoning Univ. (China)
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences (China)
Shenyang Institute of Automation, Chinese Academy of Sciences (China)
Tao Ku, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences (China)
Shenyang Institute of Automation, Chinese Academy of Sciences (China)
Hao Zhang, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences (China)
Shenyang Institute of Automation, Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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