Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics

Qian, Chunyao; Sun, Shizhao; Cui, Wei; Lou, Jian–Guang; Zhang, Haidong; Zhang, Dongmei

doi:10.1109/tvcg.2020.3030448

Cited by 41 publications

(45 citation statements)

References 22 publications

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“…Our method starts with a linear scan of the full papers published at the 2020 IEEE Visualization Conference to collect the starting points. This initial set of papers has nine papers [1]- [9]. We further augment these starting points with papers covered in related surveys [20]- [23].…”

Section: Methods and Corpusmentioning

confidence: 99%

“…Due to the interdisciplinary nature of our corpus, we find tasks are usually described in inconsistent vocabularies. For instance, the task of extracting encoding choices from visualization images or specifications is described as deconstruction [1], [2] or chart mining [23]. Thus, we wish to establish a common vocabulary that enables consistent discussions for researchers from different areas to communicate the relevance and subtleties.…”

Section: Coding and Classificationmentioning

confidence: 99%

“…Thus, we see that visualizations are becoming a new data format processed by AI. For instance, this trend was evident at the 2020 IEEE Visualization Conference where multiple techniques were proposed for automating the creation of visualizations [1]- [5], retargeting visualizations [6], [7], and analyzing visualization ensembles [8], [9]. In light of this trend, new concepts and research problems are emerging, raising the need to organize existing literature and clarify the research landscape.…”

Section: Introductionmentioning

confidence: 99%

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AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

Self Cite

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Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data. We define visualization data as the digital representations of visualizations in computers and focus on visualizations in information visualization and visual analytic. We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.

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Section: Methods and Corpusmentioning

confidence: 99%

Section: Coding and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

Self Cite

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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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“…Graphic layout generation is emerging as a new research direction for generating realistic and diverse layouts to facilitate design tasks. Recent works show promising methods of layout generation for applications such as graphic user interfaces [2], presentation slides [10], magazines [25], scientific publications [1], commercial advertisements [17,22], Computer-Aided Design (CAD) [24], indoor graphics scenes [4], etc.…”

Section: Introductionmentioning

confidence: 99%

BLT: Bidirectional Layout Transformer for Controllable Layout Generation

Kong¹,

J²,

Chang³

et al. 2021

Preprint

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Creating visual layouts is an important step in graphic design. Automatic generation of such layouts is important as we seek scale-able and diverse visual designs. Prior works on automatic layout generation focus on unconditional generation, in which the models generate layouts while neglecting user needs for specific problems. To advance conditional layout generation, we introduce BLT, a bidirectional layout transformer. BLT differs from autoregressive decoding as it first generates a draft layout that satisfies the user inputs and then refines the layout iteratively.We verify the proposed model on multiple benchmarks with various fidelity metrics. Our results demonstrate two key advances to the state-of-the-art layout transformer models. First, our model empowers layout transformers to fulfill controllable layout generation. Second, our model slashes the linear inference time in autoregressive decoding into a constant complexity, thereby achieving 4x-10x speedups in generating a layout at inference time.* Work done during their research internship at Google. (a) Conditional (top) and unconditional (bottom) generation.

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Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics

Cited by 41 publications

References 22 publications

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Towards Natural Language Interfaces for Data Visualization: A Survey

BLT: Bidirectional Layout Transformer for Controllable Layout Generation

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