Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline

Chen, Zhutian; Wang, Yun; Wang, Qianwen; Wang, Yong; Qu, Huamin

doi:10.1109/tvcg.2019.2934810

Cited by 69 publications

(61 citation statements)

References 47 publications

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“…At the synthesis step, for each layout blueprint, Text-to-Viz enumerates all extracted segments by text analyzer and then generates all valid infographics. DataShot [258], TSIs [26], Chen et al [37], and Retrieve-Then-Adapt [185] all use template-based approaches to involve natural language descriptions when automatically generating infographics to tell stories. With the advances of deep learning technology, some works leverage generative adversarial networks (GAN) [71] to Coupling from text about NBA game report to visualizations for storytelling [161].…”

Section: Narrative Storytellingmentioning

confidence: 99%

Towards Natural Language Interfaces for Data Visualization: A Survey

Shen,

Luo

et al. 2021

Preprint

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Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than worrying about operating the interface to visualization tools. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed both within academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each paper, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query understanding, data transformation, visual mapping, view transformation, human interaction, context management, and presentation. Finally, we also shed light on several promising directions for future work in the community.

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Section: Narrative Storytellingmentioning

confidence: 99%

Towards Natural Language Interfaces for Data Visualization: A Survey

Shen,

Luo

et al. 2021

Preprint

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“…For example, designers can create V v that adapts to the ambient light, visualizes in-situ temperature data, or adapt more deeply to the AR-Canvas [2]. Recent progress in computer vision on visualizations [8] offers possibilities for environment adaptive visualizations.…”

Section: Future Work and Limitationsmentioning

confidence: 99%

Augmenting Static Visualizations with PapARVis Designer

Chen

Tong

Wang

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Figure 1. Augmenting static visualizations can leverage the best of both physical and digital worlds: a) a data journalism uses an augmented static visualization to extend the space of the newspaper that is limited by the banner; b) a designer uses AR to update the outdated wall-sized timeline without recreating it; c) a tourist overlays the trajectories data on a public map in AR to see his/her moving pattern.

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“…We are also starting to see the rise of advanced techniques for the automated design of infographics and visualizations. These include automated generation of simple infographics from predefined styles adapted to data facts in a textual form [18], the automated extraction of a timeline visual template from a raster image in order to extend it [16], or the manipulation of mountain photos as line charts [32].…”

Section: Visualization and Image Processingmentioning

confidence: 99%

DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations

Zhang

Sultanum

Bezerianos

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Figure 1. Examples of creations realized with DataQuilt, a new interactive authoring tool that allows authors to borrow visual and stylistic elements from raster images and re-purpose them to create custom, pictorial visualizations. Left: a scatterplot of famous paintings by Klimt, showing the date of creation (x-axis) against how much it was sold for in auction (y-axis). Each data point is a spiral-shaped glyph whose texture is mapped to the painting it represents, whereas the Tree of Life painting is used as a decorative background. Middle: a bar chart representing the distance from the sun for the planets of our solar system. Each data point is represented by a space rocket, whose exhaust flames are stretched according to the underlying data. Decorative glyphs (sun, planets) are used for further information and visual appeal. Right: A personal visualization depicting one's coffee intake over a week. The type of coffee (espresso, latte, etc.) is represented by different coffee cups, all extracted from photographs. The orientation of the handle represents the time, whereas size is proportional to the drink size and horizontal position corresponds to the day of the week.

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Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline

Cited by 69 publications

References 47 publications

Towards Natural Language Interfaces for Data Visualization: A Survey

Towards Natural Language Interfaces for Data Visualization: A Survey

Augmenting Static Visualizations with PapARVis Designer

DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations

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