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Journal of Computer Science and Technology

, Volume 34, Issue 1, pp 155–169 | Cite as

Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

  • Yu-Ting Qiang
  • Yan-Wei Fu
  • Xiao Yu
  • Yan-Wen GuoEmail author
  • Zhi-Hua Zhou
  • Leonid Sigal
Regular Paper
  • 4 Downloads

Abstract

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.

Keywords

graphical design layout automation probabilistic graphical model 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yu-Ting Qiang
    • 1
  • Yan-Wei Fu
    • 2
  • Xiao Yu
    • 1
  • Yan-Wen Guo
    • 1
    Email author
  • Zhi-Hua Zhou
    • 1
  • Leonid Sigal
    • 3
  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Data ScienceFudan UniversityShanghaiChina
  3. 3.Disney Research PittsburghPittsburghUSA

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