2017/02/21 by Yu-ting Qiang, Yuting Qiang, Qiang, Yu-ting +11 · 9 citations
Computer Science · #Advanced Text Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Data Visualization and Analytics #FOS: Computer and information sciences #Graphics (cs.GR) #Human-Computer Interaction (cs.HC) #Multimedia (cs.MM) #Video Analysis and Summarization #cs.CV #cs.GR #cs.HC #cs.MM
paper · pdf · doi:10.48550/arxiv.1702.06228
10 pages, submission to IEEE TPAMI. arXiv admin note: text overlap with arXiv:1604.01219
arxiv created 2017/02/21 · openalex publication_date 2017/02/21 · arxiv updated 2017/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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, that 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, an MAP inference 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.