Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning

One-class svms for document classification

Larry M Manevitz and Malik Yousef. 2001 · 2001

Earlier work this paper cites.

Latent dirichlet allocation

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003

Earlier work this paper cites.

Very deep convolutional networks for large-scale image recognition

Karen Simonyan and Andrew Zisserman. 2014 · 2014

Earlier work this paper cites.

Evaluation of deep convolutional nets for document image classification and retrieval

Adam W Harley, Alex Ufkes, and Konstantinos G Derpanis. 2015 · 2015

Earlier work this paper cites.

Faster r-cnn: Towards real-time object detection with region proposal networks

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015

Earlier work this paper cites.

R-fcn: Object detection via region-based fully convolutional networks

Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. 2016 · 2016

Earlier work this paper cites.

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016

Earlier work this paper cites.

Cutting the error by half: Investigation of very deep cnn and advanced training strategies for document image classification

Muhammad Zeshan Afzal, Andreas Kölsch, Sheraz Ahmed, and Marcus Liwicki. 2017 · 2017

Earlier work this paper cites.

Self-supervised learning of visual features through embedding images into text topic spaces

Lluis Gomez, Yash Patel, Marçal Rusiñol, Dimosthenis Karatzas, and CV Jawahar. 2017 · 2017

Earlier work this paper cites.

Feature pyramid networks for object detection

Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017 · 2017

Earlier work this paper cites.

Deepdesrt: Deep learning for detection and structure recognition of tables in document images

Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. 2017 · 2017

Earlier work this paper cites.

Attention is all you need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017

Cited alongside, same era.

Learning to extract semantic structure from documents using multimodal fully convolutional neural networks

Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, and C Lee Giles. 2017 · 2017

Cited alongside, same era.

Document image classification with intra-domain transfer learning and stacked generalization of deep convolutional neural networks

Arindam Das, Saikat Roy, Ujjwal Bhattacharya, and Swapan K Parui. 2018 · 2018

Cited alongside, same era.

Bert: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018

Cited alongside, same era.

Chargrid: Towards understanding 2d documents

Anoop R Katti, Christian Reisswig, Cordula Guder, Sebastian Brarda, Steffen Bickel, Johannes Höhne, and Jean Baptiste Faddoul. 2018 · 2018

Cited alongside, same era.

Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images

Shubham Singh Paliwal, D Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. 2019 · 2019

Later among the works it cites.

Deterministic routing between layout abstractions for multi-scale classification of visually rich documents

Ritesh Sarkhel and Arnab Nandi. 2019 · 2019

Later among the works it cites.

Visual detection with context for document layout analysis

Carlos Soto and Shinjae Yoo. 2019 · 2019

Later among the works it cites.

Cutie: Learning to understand documents with convolutional universal text information extractor

Xiaohui Zhao, Endi Niu, Zhuo Wu, and Xiaoguang Wang. 2019 · 2019

Later among the works it cites.

Publaynet: largest dataset ever for document layout analysis

Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. 2019 · 2019

Later among the works it cites.