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Charts are a powerful tool for visually conveying complex data, but their comprehension poses a challenge due to the diverse chart types and intricate components.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Describing complex charts in natural language: A caption generation system
Vibhu O Mittal, Johanna D Moore, Giuseppe Carenini, and Steven Roth · 1998
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Intentions in the coordinated generation of graphics and text from tabular data
Massimo Fasciano and Guy Lapalme · 2000
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Autobrief: an experimental system for the automatic generation of briefings in integrated text and information graphics
Nancy L Green, Giuseppe Carenini, Stephan Kerpedjiev, Joe Mattis, Johanna D Moore, and Steven F Roth · 2004
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An architecture for data-to-text systems
Ehud Reiter · 2007
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Revision: Automated classification, analysis and redesign of chart images
Manolis Savva, Nicholas Kong, Arti Chhajta, Li Fei-Fei, Maneesh Agrawala, and Jeffrey Heer · 2011
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Summarizing information graphics textually
Seniz Demir, Sandra Carberry, and Kathleen F McCoy · 2012
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View: Visual information extraction widget for improving chart images accessibility
Jinglun Gao, Yin Zhou, and Kenneth E Barner · 2012
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Evaluating a tool for improving accessibility to charts and graphs
Leo Ferres, Gitte Lindgaard, Livia Sumegi, and Bruce Tsuji · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Fast r-cnn
Ross Girshick · 2015
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Stacked hourglass networks for human pose estimation
Alejandro Newell, Kaiyu Yang, and Jia Deng · 2016
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Soft-nms–improving object detection with one line of code
Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Chartsense: Interactive data extraction from chart images
Daekyoung Jung, Wonjae Kim, Hyunjoo Song, Jeong-in Hwang, Bongshin Lee, Bohyoung Kim, and Jinwook Seo · 2017
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Figureqa: An annotated figure dataset for visual reasoning
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, and Yoshua Bengio · 2017
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Reverse-engineering visualizations: Recovering visual encodings from chart images
Jorge Poco and Jeffrey Heer · 2017
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A corpus of natural language for visual reasoning
Alane Suhr, Mike Lewis, James Yeh, and Yoav Artzi · 2017
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Chart-text: A fully automated chart image descriptor
Abhijit Balaji, Thuvaarakkesh Ramanathan, and Venkateshwarlu Sonathi · 2018
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Learning to transfer: Generalizable attribute learning with multitask neural model search
Zhi-Qi Cheng, Xiao Wu, Siyu Huang, Jun-Xiu Li, Alexander G Hauptmann, and Qiang Peng · 2018
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Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan · 2018
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Hei Law and Jia Deng · 2018
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Augmenting visualizations with interactive data facts to facilitate interpretation and communication
Arjun Srinivasan, Steven M Drucker, Alex Endert, and John Stasko · 2018
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Chartocr: data extraction from charts images via a deep hybrid framework
Junyu Luo, Zekun Li, Jinpeng Wang, and Chin-Yew Lin · 2021
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Chartreader: Automatic parsing of bar-plots
Chinmayee Rane, Seshasayee Mahadevan Subramanya, Devi Sandeep Endluri, Jian Wu, and C Lee Giles · 2021
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A survey on ml4vis: Applying machinelearning advances to data visualization
Qianwen Wang, Zhutian Chen, Yong Wang, and Huamin Qu · 2021
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Latr: Layout-aware transformer for scene-text vqa
Ali Furkan Biten, Ron Litman, Yusheng Xie, Srikar Appalaraju, and R Manmatha · 2022
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Pali: A jointly-scaled multilingual language-image model
Xi Chen, Xiao Wang, Soravit Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, et al · 2022
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Charles Chen, Ruiyi Zhang, Eunyee Koh, Sungchul Kim, Scott Cohen, Tong Yu, Ryan Rossi, and Razvan Bunescu · 2019
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Visualizing for the non-visual: Enabling the visually impaired to use visualization
Jinho Choi, Sanghun Jung, Deok Gun Park, Jaegul Choo, and Niklas Elmqvist · 2019
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Datasite: Proactive visual data exploration with computation of insight-based recommendations
Zhe Cui, Sriram Karthik Badam, M Adil Yalçin, and Niklas Elmqvist · 2019
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Data extraction from charts via single deep neural network
Xiaoyi Liu, Diego Klabjan, and Patrick NBless · 2019
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Alane Suhr and Yoav Artzi · 2019
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Datashot: Automatic generation of fact sheets from tabular data
Yun Wang, Zhida Sun, Haidong Zhang, Weiwei Cui, Ke Xu, Xiaojuan Ma, and Dongmei Zhang · 2019
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Answering questions about data visualizations using efficient bimodal fusion
Kushal Kafle, Robik Shrestha, Scott Cohen, Brian Price, and Christopher Kanan · 2020
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Gsrformer: Grounded situation recognition transformer with alternate semantic attention refinement
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Opencqa: Open-ended question answering with charts
Shankar Kantharaj, Xuan Long Do, Rixie Tiffany Ko Leong, Jia Qing Tan, Enamul Hoque, and Shafiq Joty · 2022
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Parsing line chart images using linear programming
Hajime Kato, Mitsuru Nakazawa, Hsuan-Kung Yang, Mark Chen, and Bj · 2022
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Prestu: Pre-training for scene-text understanding
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Ocr-free document understanding transformer
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Pix2struct: Screenshot parsing as pretraining for visual language understanding
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Classification-regression for chart comprehension
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Deplot: One-shot visual language reasoning by plot-to-table translation
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Matcha: Enhancing visual language pretraining with math reasoning and chart derendering
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Linecap: Line charts for data visualization captioning models
Anita Mahinpei, Zona Kostic, and Chris Tanner · 2022
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Chartqa: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque · 2022
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Dl4scivis: A state-of-the-art survey on deep learning for scientific visualization
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Git: A generative image-to-text transformer for vision and language
Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, and Lijuan Wang · 2022
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Coca: Contrastive captioners are image-text foundation models
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End-to-end document recognition and understanding with dessurt
Brian Davis, Bryan Morse, Brian Price, Chris Tensmeyer, Curtis Wigington, and Vlad Morariu · 2023
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