VIS30K: A Collection of Figures and Tables from IEEE Visualization Conference Publications
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Abstract (arXiv)
We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 5; 2025: 2; 2026: 1.
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- 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 158
- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuningcited by 27
- 2022Chart Question Answering: State of the Art and Future Directionscited by 26
- 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understandingcited by 26
- 2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
- 2023InvVis: Large-Scale Data Embedding for Invertible Visualizationcited by 6
- 2024V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestionscited by 3
- 2021Document Domain Randomization for Deep Learning Document Layout Extractioncited by 1
- 2022Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Papercited by 1
- 2024An Image-based Typology for Visualizationcited by 1
- 2025Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legendcited by 1
- 2025Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorizationcited by 0
- 2026FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Exploration17 Sep 2026
- 2025Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorization6 Sep 2025
- 2025Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend28 Jul 2025
- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning29 Jul 2024
- 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding6 Jul 2024
- 2024V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions21 Jun 2024
- 2024Generative AI for Visualization: State of the Art and Future Directions28 Apr 2024
- 2024An Image-based Typology for Visualization7 Mar 2024
- 2023InvVis: Large-Scale Data Embedding for Invertible Visualization30 Jul 2023
- 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning24 May 2023
- 2022Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper22 Sep 2022
- 2022Chart Question Answering: State of the Art and Future Directions8 May 2022
1 more
- 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 158
- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuningcited by 27
- 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understandingcited by 26
- 2025Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorizationcited by 0
- 2025Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorization6 Sep 2025
- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning29 Jul 2024
- 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding6 Jul 2024
- 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning24 May 2023
- 2022Chart Question Answering: State of the Art and Future Directionscited by 26
- 2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
- 2023InvVis: Large-Scale Data Embedding for Invertible Visualizationcited by 6
- 2024V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestionscited by 3
- 2021Document Domain Randomization for Deep Learning Document Layout Extractioncited by 1
- 2022Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Papercited by 1
- 2024An Image-based Typology for Visualizationcited by 1
- 2025Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legendcited by 1
- 2025Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend28 Jul 2025
- 2024V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions21 Jun 2024
- 2024Generative AI for Visualization: State of the Art and Future Directions28 Apr 2024
- 2024An Image-based Typology for Visualization7 Mar 2024
- 2023InvVis: Large-Scale Data Embedding for Invertible Visualization30 Jul 2023
- 2022Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper22 Sep 2022
- 2022Chart Question Answering: State of the Art and Future Directions8 May 2022
- 2021Document Domain Randomization for Deep Learning Document Layout Extraction20 May 2021