Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (1): 155-169.doi: 10.1007/s11390-019-1904-1

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Data Management and Data Mining • Previous Articles     Next Articles

Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

Yu-Ting Qiang1, Yan-Wei Fu2, Member, ACM, IEEE, Xiao Yu1, Yan-Wen Guo1,*, Member, IEEE Zhi-Hua Zhou1, Fellow, CCF, ACM, IEEE, and Leonid Sigal3, Member, ACM, IEEE   

  1. 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, China;
    2 School of Data Science, Fudan University, Shanghai 200433, China;
    3 Disney Research Pittsburgh, Pittsburgh 15241, U.S.A.
  • Received:2018-01-14 Revised:2018-11-12 Online:2019-01-05 Published:2019-01-12
  • Contact: Yan-Wen Guo E-mail:ywguo.nju@gmail.com
  • About author:Yu-Ting Qiang is a Ph.D. student at the National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing. She received her B.S. degree in software engineering from Jilin University, Changchun, in 2013. Her research interests include computer vision and machine learning.
  • Supported by:
    This work was supported by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20150016, the National Natural Science Foundation of China under Grant Nos. 61772257 and 61672279, and the Fundamental Research Funds for the Central Universities of China under Grant No. 020214380042.

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Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time-consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, which utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements, are learned and inferred from data. During the inference stage, the maximum a posterior (MAP) estimation framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.

Key words: graphical design; layout automation; probabilistic graphical model;

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