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Open AccessArticle

Social Image Captioning: Exploring Visual Attention and User Attention

College of Computer & Communication Engineering, China University of Petroleum (East China), Qingdao 266555, China
First Research Institute of the Ministry of Public Security of PRC, Beijing 100048, China
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 646;
Received: 1 December 2017 / Revised: 9 February 2018 / Accepted: 12 February 2018 / Published: 22 February 2018
(This article belongs to the Special Issue Sensing, Data Analysis and Platforms for Ubiquitous Intelligence)
Image captioning with a natural language has been an emerging trend. However, the social image, associated with a set of user-contributed tags, has been rarely investigated for a similar task. The user-contributed tags, which could reflect the user attention, have been neglected in conventional image captioning. Most existing image captioning models cannot be applied directly to social image captioning. In this work, a dual attention model is proposed for social image captioning by combining the visual attention and user attention simultaneously.Visual attention is used to compress a large mount of salient visual information, while user attention is applied to adjust the description of the social images with user-contributed tags. Experiments conducted on the Microsoft (MS) COCO dataset demonstrate the superiority of the proposed method of dual attention. View Full-Text
Keywords: social image captioning; user-contributed tags; user attention; visual attention social image captioning; user-contributed tags; user attention; visual attention
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MDPI and ACS Style

Wang, L.; Chu, X.; Zhang, W.; Wei, Y.; Sun, W.; Wu, C. Social Image Captioning: Exploring Visual Attention and User Attention. Sensors 2018, 18, 646.

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