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Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU).
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Building a test collection for complex document information processing
David Lewis, Gady Agam, Shlomo Argamon, Ophir Frieder, David Grossman, and Jefferson Heard. 2006 · 2006
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Evaluation of deep convolutional nets for document image classification and retrieval
Adam W Harley, Alex Ufkes, and Konstantinos G Derpanis. 2015 · 2015
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova. 2019 · 2019
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Icdar2019 competition on scanned receipt ocr and information extraction
Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawahar. 2019 · 2019
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Funsd: A dataset for form understanding in noisy scanned documents
Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran. 2019 · 2019
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Cord: a consolidated receipt dataset for post-ocr parsing
Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, and Hwalsuk Lee. 2019 · 2019
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Unilmv2: Pseudo-masked language models for unified language model pre-training
Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Jianfeng Gao, Songhao Piao, Ming Zhou, et al. 2020 · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020 · 2020
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Layoutlm: Pre-training of text and layout for document image understanding
Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. 2020 · 2020
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StructuralLM: Structural pre-training for form understanding, author = ”li, chenliang and bi, bin and yan, ming and wang, wei and huang, songfang and huang, fei and si, luo
2021 · 2021
Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. 2021 · 2021
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Going full-tilt boogie on document understanding with text-image-layout transformer
Rafał Powalski, Łukasz Borchmann, Dawid Jurkiewicz, Tomasz Dwojak, Michał Pietruszka, and Gabriela Pałka. 2021 · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
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LayoutReader: Pre-training of text and layout for reading order detection
Zilong Wang, Yiheng Xu, Lei Cui, Jingbo Shang, and Furu Wei. 2021 · 2021
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Layoutlmv3: Pre-training for document ai with unified text and image masking
Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, and Furu Wei. 2022 · 2022
Closest in time.
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Cited alongside, same era.
Docformer: End-to-end transformer for document understanding
Srikar Appalaraju, Bhavan Jasani, Bhargava Urala Kota, Yusheng Xie, and R Manmatha. 2021 · 2021
Cited alongside, same era.
Document ai: Benchmarks, models and applications
Lei Cui, Yiheng Xu, Tengchao Lv, and Furu Wei. 2021 · 2021
Cited alongside, same era.
Unidoc: Unified pretraining framework for document understanding
Jiuxiang Gu, Jason Kuen, Vlad I Morariu, Handong Zhao, Rajiv Jain, Nikolaos Barmpalios, Ani Nenkova, and Tong Sun. 2021 · 2021
Cited alongside, same era.
Mindspore
Cited in the paper.
Wukong: 100 million large-scale chinese cross-modal pre-training dataset and a foundation framework
Jiaxi Gu, Xiaojun Meng, Guansong Lu, Lu Hou, Minzhe Niu, Hang Xu, Xiaodan Liang, Wei Zhang, Xin Jiang, and Chunjing Xu. 2022a
Cited in the paper.
Xylayoutlm: Towards layout-aware multimodal networks for visually-rich document understanding
Zhangxuan Gu, Changhua Meng, Ke Wang, Jun Lan, Weiqiang Wang, Ming Gu, and Liqing Zhang. 2022b
Cited in the paper.
Dit: Self-supervised pre-training for document image transformer
Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, and Furu Wei. 2022a
Cited in the paper.
Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Teng Hu, Weichong Yin, Yongfeng Chen, Yin Zhang, et al. 2022 · 2022
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Filip: Fine-grained interactive language-image pre-training
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. 2022 · 2022
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