Scientific poster generation: A new dataset and approach

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Pub Date : 2025-03-05 DOI:10.1016/j.patcog.2025.111507

Xinyi Zhong , Zusheng Tan , Jing Li , Shen Gao , Jing Ma , Shanshan Feng , Billy Chiu

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引用次数: 0

Abstract

Automating poster creation from research papers saves scientists time. However, training models for this task is challenging due to limited datasets. Moreover, existing methods are mostly rule/template-based, which lack the flexibility to adapt to different content and design requirements in scientific posters. Our contributions aim to address these issues. We introduce Sci-PosterLayout, a dataset comprising 1,226 scientific posters with greater variety in content, layout and domains. Using a template-free method with a seq2seq model and Design Pattern Schema (DPS), we learn various content and design patterns for poster layout generation. Evaluations against existing methods and datasets show our approach produces high-quality posters with diverse layouts. Our work seeks to advance research in scientific poster generation by building a new dataset and proposing template-free methods that require minimal human intervention. The Sci-PosterLayout dataset will be publicly available at https://github.com/kitman0000/Sci-PosterLayout-Data.

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来源期刊

Pattern Recognition 工程技术-工程：电子与电气

CiteScore

14.40

自引率

16.20%

发文量

683

审稿时长

5.6 months

期刊介绍： The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.