2023

ACL-Fig: A Dataset for Scientific Figure Classification

Karishma, Zeba, Rohatgi, Shaurya, Puranik, Kavya Shrinivas et al.

Understand

Most existing large-scale academic search engines are built to retrieve text-based information.

However, there are no large-scale retrieval services for scientific figures and tables.
One challenge for such services is understanding scientific figures' semantics, such as their types and purposes.
A key obstacle is the need for datasets containing annotated scientific figures and tables, which can then be used for classification, question-answering, and auto-captioning.

Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Peter J. Rousseeuw · 1987
Earlier work this paper cites.
Hough technique for bar charts detection and recognition in document images
Y. Zhou and C. Tan · 2000
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Revision: automated classification, analysis and redesign of chart images
M. Savva, Nicholas Kong, Arti Chhajta, Li Fei-Fei, Maneesh Agrawala, and J. Heer · 2011
Earlier work this paper cites.
Machine learning classification algorithms to recognize chart types in portable document format (pdf) files
V. Karthikeyani and S. Nagarajan · 2012
Earlier work this paper cites.
The number of scholarly documents on the public web
Madian Khabsa and C. Lee Giles · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Original
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.

Pdfmef: A multi-entity knowledge extraction framework for scholarly documents and semantic search
Jian Wu, Jason Killian, Huaiyu Yang, Kyle Williams, Sagnik Ray Choudhury, Suppawong Tuarob, Cornelia Caragea, and C. Lee Giles · 2015
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Figureseer: Parsing result-figures in research papers
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Deepchart: Combining deep convolutional networks and deep belief networks in chart classification
Binbin Tang, Xiao Liu, Jie Lei, Mingli Song, Dapeng Tao, Shuifa Sun, and Fangmin Dong · 2016
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Extracting scientific figures with distantly supervised neural networks
Noah Siegel, Nicholas Lourie, Russell Power, and Waleed Ammar · 2018
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Figureqa: An annotated figure dataset for visual reasoning, 2018
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Akos Kadar, Adam Trischler, and Yoshua Bengio · 2018
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Docfigure: A dataset for scientific document figure classification
K. V. Jobin, Ajoy Mondal, and C. V. Jawahar · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Original
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Coca: Contrastive captioners are image-text foundation models
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Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu · 2022
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