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In scholarly documents, figures provide a straightforward way of communicating scientific findings to readers.
Figure captioning with reasoning and sequence-level training
Chen, C.; Zhang, R.; Koh, E.; Kim, S.; Cohen, S.; Yu, T.; Rossi, R.; and Bunescu, R. 2019 · 1906
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Zhang, Q.; Wang, C.; Xin, C.; and Wu, H. 2019 · 1911
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Bleu: a Method for Automatic Evaluation of Machine Translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. 2004 · 2004
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METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Banerjee, S.; and Lavie, A. 2005 · 2005
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Word spotting and recognition with embedded attributes
Almazán, J.; Gordo, A.; Fornés, A.; and Valveny, E. 2014 · 2014
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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Cider: Consensus-based image description evaluation
Vedantam, R.; Lawrence Zitnick, C.; and Parikh, D. 2015 · 2015
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Spice: Semantic propositional image caption evaluation
Anderson, P.; Fernando, B.; Johnson, M.; and Gould, S. 2016 · 2016
Cited alongside, same era.
Pdffigures 2.0: Mining figures from research papers
Clark, C.; and Divvala, S. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Figureqa: An annotated figure dataset for visual reasoning
Kahou, S. E.; Michalski, V.; Atkinson, A.; Kádár, Á.; Trischler, A.; and Bengio, Y. 2017 · 2017
Cited alongside, same era.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I.; Lo, K.; and Cohan, A. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Figure Captioning with Relation Maps for Reasoning
Chen, C.; Zhang, R.; Koh, E.; Kim, S.; Cohen, S.; and Rossi, R. 2020 · 2020
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Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA
Hu, R.; Singh, A.; Darrell, T.; and Rohrbach, M. 2020 · 2020
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MMF: A multimodal framework for vision and language research
Singh, A.; Goswami, V.; Natarajan, V.; Jiang, Y.; Chen, X.; Shah, M.; Rohrbach, M.; Batra, D.; and Parikh, D. 2020 · 2020
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SciCap: Generating Captions for Scientific Figures
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Kudo, T.; and Richardson, J. 2018 · 2018
Cited alongside, same era.
Enriching Word Vectors with Subword Information
Bojanowski, P.; Grave, E.; Joulin, A.; and Mikolov, T. 2017a
Cited in the paper.
Enriching Word Vectors with Subword Information
Bojanowski, P.; Grave, E.; Joulin, A.; and Mikolov, T. 2017b
Cited in the paper.
Textcaps: a dataset for image captioning with reading comprehension
Sidorov, O.; Hu, R.; Rohrbach, M.; and Singh, A. 2020a
Cited in the paper.
TextCaps: a Dataset for Image Captioningwith Reading Comprehension
Sidorov, O.; Hu, R.; Rohrbach, M.; and Singh, A. 2020b
Cited in the paper.
Hsu, T.-Y.; Giles, C. L.; and Huang, T.-H. 2021 · 2021
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