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- W2949454564 abstract "Multimodal documents occur in a variety of forms, asgraphs in technical reports, diagrams in textbooks, and graphicdesigns in bulletins. Humans can efficiently process the visual andtextual information contained within to make decisions on topicsincluding business, healthcare, and science. Building thecomputational tools to understand multimodal documents can haveimportant applications for web search, information retrieval,captioning and summarization, and automated design. This thesismakes contributions on two fronts: (i) to the development of datacollection methods for measuring how humans perceive multimodaldocuments (i.e., where they look, what they find important), and(ii) to the development of computer vision tools for automaticallyparsing and making predictions about multimodal documents (i.e.,the subject matter they are about). Specifically, the crowdsourcedattention data captured from our novel user interfaces is used totrain neural network models to predict where people look in graphicdesigns and information visualizations, with demonstratedapplications to thumbnailing, design retargeting, and interactivefeedback within graphic design tools. Separately, our models fordetecting visual elements and parsing text elements in infographics(information graphics) are used for topic prediction and to presenta system for automatic summarization. This thesis makescontributions at the interface of human and computer vision, withapplications to human-computer interfaces anddesign." @default.
- W2949454564 created "2019-06-27" @default.
- W2949454564 creator A5011292436 @default.
- W2949454564 date "2018-01-01" @default.
- W2949454564 modified "2023-09-27" @default.
- W2949454564 title "Computational perception for multi-modal document understanding" @default.
- W2949454564 hasPublicationYear "2018" @default.
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