This repo provides a Pytorch implementation for Recurrent Comparator with attention models to detect counterfeit documents.
@article{aberenguel_recurrent_counterfeit,
author = { Albert Berenguel and
Oriol Ramos Terrades and
Josep Llados and
Cristina Canero},
title = {Recurrent Comparator with attention models to detect counterfeit documents},
booktitle={ICDAR},
year = {2019},
journal={Proc. IEEE}
}
- Albert Berenguel (@aberenguel) Webpage
The initial idea of this paper was initially from Attentive Recurrent Comparators paper. Special thanks to Pranav Shyam and Sanyam Agarwal for their Theano and Pytorch implementation in which this work is based on. Additionally the ideas from Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification paper were adapted. For more details: https://github.com/pranv/ARC https://github.com/sanyam5/arc-pytorch
@article{DBLP:journals/corr/ShyamGD17,
author = {Pranav Shyam and
Shubham Gupta and
Ambedkar Dukkipati},
title = {Attentive Recurrent Comparators},
journal = {CoRR},
volume = {abs/1703.00767},
year = {2017},
url = {http://arxiv.org/abs/1703.00767},
timestamp = {Wed, 07 Jun 2017 14:42:50 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/ShyamGD17},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{wu2018deep,
title={Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification},
author={Wu, Lin and Wang, Yang and Gao, Junbin and Tao, Dacheng},
journal={arXiv preprint arXiv:1804.11027},
year={2018}
}