seamless.bib

@inproceedings{hui2018liteflownet,
  title={Liteflownet: A lightweight convolutional neural network for optical flow estimation},
  author={Hui, Tak-Wai and Tang, Xiaoou and Change Loy, Chen},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={8981--8989},
  year={2018}
}

@inproceedings{ranjan2017optical,
  title={Optical Flow Estimation Using a Spatial Pyramid Network},
  author={Ranjan, Anurag and Black, Michael J},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4161--4170},
  year={2017}
}

@article{saalfeld2012elastic,
  title={Elastic volume reconstruction from series of ultra-thin microscopy sections},
  author={Saalfeld, Stephan and Fetter, Richard and Cardona, Albert and Tomancak, Pavel},
  journal={Nature methods},
  volume={9},
  number={7},
  pages={717},
  year={2012},
  publisher={Nature Publishing Group}
}

@article{balakrishnan2018unsupervised,
  title={An Unsupervised Learning Model for Deformable Medical Image Registration},
  author={Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R and Guttag, John and Dalca, Adrian V},
  journal={arXiv preprint arXiv:1802.02604},
  year={2018}
}

@incollection{yoo2017ssemnet,
  title={ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features},
  author={Yoo, Inwan and Hildebrand, David GC and Tobin, Willie F and Lee, Wei-Chung Allen and Jeong, Won-Ki},
  booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
  pages={249--257},
  year={2017},
  publisher={Springer}
}

@article{zheng2017complete,
  title={A complete electron microscopy volume of the brain of adult Drosophila melanogaster},
  author={Zheng, Zhihao and Lauritzen, J Scott and Perlman, Eric and Robinson, Camenzind G and Nichols, Matthew and Milkie, Daniel and Torrens, Omar and Price, John and Fisher, Corey B and Sharifi, Nadiya and others},
  journal={BioRxiv},
  pages={140905},
  year={2017},
  publisher={Cold Spring Harbor Laboratory}
}

@InProceedings{garcia2007quality,
	title = 	 {TOWARDS OBJECTIVE QUALITY ASSESSMENT OF
	IMAGE REGISTRATION RESULTS},
	author = 	 {Birgit Moller and Rafael Garcia},
	booktitle = 	 {International Conference on Computer Vision Theory and Applications},
	year = 	 {2007},
	publisher = 	 {VISAPP},
	pdf = 	 {http://users.informatik.uni-halle.de/~moeller/pubs/pdf/Moeller07_VISAPP.pdf},
	url = 	 {http://users.informatik.uni-halle.de/~moeller/pubs/pdf/Moeller07_VISAPP.pdf},
}

@article{flownet,
  author    = {Philipp Fischer and
               Alexey Dosovitskiy and
               Eddy Ilg and
               Philip H{\"{a}}usser and
               Caner Hazirbas and
               Vladimir Golkov and
               Patrick van der Smagt and
               Daniel Cremers and
               Thomas Brox},
  title     = {FlowNet: Learning Optical Flow with Convolutional Networks},
  journal   = {CoRR},
  volume    = {abs/1504.06852},
  year      = {2015},
  url       = {http://arxiv.org/abs/1504.06852},
  archivePrefix = {arXiv},
  eprint    = {1504.06852},
  timestamp = {Wed, 07 Jun 2017 14:41:04 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/FischerDIHHGSCB15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{chopra2005learning,
  title={Learning a similarity metric discriminatively, with application to face verification},
  author={Chopra, Sumit and Hadsell, Raia and LeCun, Yann},
  booktitle={Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
  volume={1},
  pages={539--546},
  year={2005},
  organization={IEEE}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@incollection{GANs,
	title = {Generative Adversarial Nets},
	author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
	booktitle = {Advances in Neural Information Processing Systems 27},
	editor = {Z. Ghahramani and M. Welling and C. Cortes and N. D. Lawrence and K. Q. Weinberger},
	pages = {2672--2680},
	year = {2014},
	publisher = {Curran Associates, Inc.},
	url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf}
}

@misc{tommy,
	author = "Macrina, Tommy",
	date = "2018-02-07",
	howpublished = "personal communication"
}

@article{UNet,
	author    = {Olaf Ronneberger and
	Philipp Fischer and
	Thomas Brox},
	title     = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
	journal   = {CoRR},
	volume    = {abs/1505.04597},
	year      = {2015},
	url       = {http://arxiv.org/abs/1505.04597},
	archivePrefix = {arXiv},
	eprint    = {1505.04597},
	timestamp = {Wed, 07 Jun 2017 14:40:33 +0200},
	biburl    = {https://dblp.org/rec/bib/journals/corr/RonnebergerFB15},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{paszke2017automatic,
	title={Automatic differentiation in PyTorch},
	author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
	booktitle={NIPS-W},
	year={2017}
}

@inproceedings{maas2013rectifier,
  title={Rectifier nonlinearities improve neural network acoustic models},
  author={Maas, Andrew L and Hannun, Awni Y and Ng, Andrew Y},
  booktitle={Proc. icml},
  volume={30},
  pages={3},
  year={2013}
}

@inproceedings{metric,
	doi = {10.1117/12.840389},
	url = {https://doi.org/10.1117/12.840389},
	year  = {2010},
	month = {mar},
	publisher = {{SPIE}},
	author = {A. Melbourne and G. Ridgway and D. J. Hawkes},
	editor = {Benoit M. Dawant and David R. Haynor},
	title = {Image similarity metrics in image registration},
	booktitle = {Medical Imaging 2010: Image Processing}
}

@article{rectifier,
	author    = {Kaiming He and
	Xiangyu Zhang and
	Shaoqing Ren and
	Jian Sun},
	title     = {Delving Deep into Rectifiers: Surpassing Human-Level Performance on
	ImageNet Classification},
	journal   = {CoRR},
	volume    = {abs/1502.01852},
	year      = {2015},
	url       = {http://arxiv.org/abs/1502.01852},
	archivePrefix = {arXiv},
	eprint    = {1502.01852},
	timestamp = {Wed, 07 Jun 2017 14:41:19 +0200},
	biburl    = {https://dblp.org/rec/bib/journals/corr/HeZR015},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{quality,
	title = 	 {TOWARDS OBJECTIVE QUALITY ASSESSMENT OF
	IMAGE REGISTRATION RESULTS},
	author = 	 {Birgit Moller, Rafael Garcia},
	booktitle = 	 {International Conference on Computer Vision Theory and Applications},
	year = 	 {2007},
	publisher = 	 {VISAPP},
	pdf = 	 {http://users.informatik.uni-halle.de/~moeller/pubs/pdf/Moeller07_VISAPP.pdf},
	url = 	 {http://users.informatik.uni-halle.de/~moeller/pubs/pdf/Moeller07_VISAPP.pdf},
}

@InProceedings{Glorot,
	title = 	 {Understanding the difficulty of training deep feedforward neural networks},
	author = 	 {Xavier Glorot and Yoshua Bengio},
	booktitle = 	 {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
	pages = 	 {249--256},
	year = 	 {2010},
	editor = 	 {Yee Whye Teh and Mike Titterington},
	volume = 	 {9},
	series = 	 {Proceedings of Machine Learning Research},
	address = 	 {Chia Laguna Resort, Sardinia, Italy},
	month = 	 {13--15 May},
	publisher = 	 {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf},
	url = 	 {http://proceedings.mlr.press/v9/glorot10a.html},
	abstract = 	 {Whereas before 2006 it appears that deep multi-layer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.  We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1.  Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence.}
}

@incollection{burt1987laplacian,
  title={The Laplacian pyramid as a compact image code},
  author={Burt, Peter J and Adelson, Edward H},
  booktitle={Readings in Computer Vision},
  pages={671--679},
  year={1987},
  publisher={Elsevier}
}

@ARTICLE{jain:2016,
	author = {{Jain}, V.},
	title = "{Adversarial Image Alignment and Interpolation}",
	journal = {ArXiv e-prints},
	archivePrefix = "arXiv",
	eprint = {1707.00067},
	primaryClass = "cs.CV",
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	year = 2017,
	month = jun,
	adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170700067J},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@MISC{eyewire,
	author = {{EyeWire}},
	title = {Carl Zimmer writes about EyeWire in Discover Magazine},
	year = {2012},
	note = {[Online; accessed May 2, 2018]},
	url = {http://blog.eyewire.org/brain-connections-may-be-key/}
}

@article{fukushima:neocognitronbc,
	added-at = {2008-03-11T14:52:34.000+0100},
	author = {Fukushima, Kunihiko},
	biburl = {https://www.bibsonomy.org/bibtex/29ecd878c4827c46dab6b9622cfa00072/idsia},
	citeulike-article-id = {2376719},
	interhash = {303975e6400e477e91c91e7dc2c47544},
	intrahash = {9ecd878c4827c46dab6b9622cfa00072},
	journal = {Biological Cybernetics},
	keywords = {nn},
	pages = {193--202},
	priority = {2},
	timestamp = {2008-03-11T15:04:22.000+0100},
	title = {{N}eocognitron: {A} Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position},
	volume = 36,
	year = 1980
}

@article{ResNet,
	author    = {Kaiming He and
	Xiangyu Zhang and
	Shaoqing Ren and
	Jian Sun},
	title     = {Deep Residual Learning for Image Recognition},
	journal   = {CoRR},
	volume    = {abs/1512.03385},
	year      = {2015},
	url       = {http://arxiv.org/abs/1512.03385},
	archivePrefix = {arXiv},
	eprint    = {1512.03385},
	timestamp = {Wed, 07 Jun 2017 14:41:17 +0200},
	biburl    = {https://dblp.org/rec/bib/journals/corr/HeZRS15},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{brainneurons,
	doi = {10.3389/neuro.09.031.2009},
	url = {https://doi.org/10.3389/neuro.09.031.2009},
	year  = {2009},
	publisher = {Frontiers Media {SA}},
	volume = {3},
	author = {Suzana Herculano-Houzel},
	title = {The human brain in numbers: a linearly scaled-up primate brain},
	journal = {Frontiers in Human Neuroscience}
}

@article{ynot,
	doi = {10.1038/nmeth.2480},
	url = {https://doi.org/10.1038/nmeth.2480},
	year  = {2013},
	month = {jun},
	publisher = {Springer Nature},
	volume = {10},
	number = {6},
	pages = {494--500},
	author = {Joshua L Morgan and Jeff W Lichtman},
	title = {Why not connectomics?},
	journal = {Nature Methods}
}

@article{brainsynapses,
	doi = {10.5038/2326-3652.3.1.26},
	url = {https://doi.org/10.5038/2326-3652.3.1.26},
	year  = {2013},
	month = {may},
	publisher = {University of South Florida Libraries},
	volume = {3},
	number = {1},
	author = {Thai Nguyen},
	title = {Total Number of Synapses in the Adult Human Neocortex},
	journal = {Undergraduate Journal of Mathematical Modeling: One $+$ Two}
}

@article{CElegans,
	doi = {10.1098/rstb.1986.0056},
	url = {https://doi.org/10.1098/rstb.1986.0056},
	year  = {1986},
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	publisher = {The Royal Society},
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	author = {J. G. White and E. Southgate and J. N. Thomson and S. Brenner},
	title = {The Structure of the Nervous System of the Nematode Caenorhabditis elegans},
	journal = {Philosophical Transactions of the Royal Society B: Biological Sciences}
}

@incollection{AlexNet,
	title = {ImageNet Classification with Deep Convolutional Neural Networks},
	author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
	booktitle = {Advances in Neural Information Processing Systems 25},
	editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
	pages = {1097--1105},
	year = {2012},
	publisher = {Curran Associates, Inc.},
	url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}
}


@incollection{LeCun1999,
	doi = {10.1007/3-540-46805-6\_19},
	url = {https://doi.org/10.1007/3-540-46805-6\_19},
	year  = {1999},
	publisher = {Springer Berlin Heidelberg},
	pages = {319--345},
	author = {Yann LeCun and Patrick Haffner and L{\'{e}}on Bottou and Yoshua Bengio},
	title = {Object Recognition with Gradient-Based Learning},
	booktitle = {Shape,  Contour and Grouping in Computer Vision}
}

@ARTICLE{jaderberg:2015,
	author = {{Jaderberg}, M. and {Simonyan}, K. and {Zisserman}, A. and {Kavukcuoglu}, K.
	},
	title = "{Spatial Transformer Networks}",
	journal = {ArXiv e-prints},
	archivePrefix = "arXiv",
	eprint = {1506.02025},
	primaryClass = "cs.CV",
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	year = 2015,
	month = jun,
	adsurl = {http://adsabs.harvard.edu/abs/2015arXiv150602025J},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{receptivefield,
	author    = {Wenjie Luo and
	Yujia Li and
	Raquel Urtasun and
	Richard S. Zemel},
	title     = {Understanding the Effective Receptive Field in Deep Convolutional
	Neural Networks},
	journal   = {CoRR},
	volume    = {abs/1701.04128},
	year      = {2017},
	url       = {http://arxiv.org/abs/1701.04128},
	archivePrefix = {arXiv},
	eprint    = {1701.04128},
	timestamp = {Wed, 07 Jun 2017 14:41:26 +0200},
	biburl    = {https://dblp.org/rec/bib/journals/corr/LuoLUZ17},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{tv, 
	author={V. Vishnevskiy and T. Gass and G. Szekely and C. Tanner and O. Goksel}, 
	journal={IEEE Transactions on Medical Imaging}, 
	title={Isotropic Total Variation Regularization of Displacements in Parametric Image Registration}, 
	year={2017}, 
	volume={36}, 
	number={2}, 
	pages={385-395}, 
	keywords={biomedical MRI;image registration;lung;medical image processing;pneumodynamics;time series;ADMM;Alternating Directions Method of Multipliers;CT lung images;MR liver images;Tikhonov regularization;abdomen;average target registration error;breathing motion databases;clinical databases;displacements;ill-posed problem;image time-series;implausible displacement fields;isotropic total variation regularization;local minima;nonsmooth displacement fields;optimization;organ masks;parametric image registration;respiration;sliding interfaces;spatial regularization;thorax;Databases;Image registration;Lungs;Measurement;Motion segmentation;Optimization;TV;4DCT;4DMR;ADMM;breathing motion;sliding at anatomical interfaces;Algorithms;Four-Dimensional Computed Tomography;Motion}, 
	doi={10.1109/TMI.2016.2610583}, 
	ISSN={0278-0062}, 
	month={Feb},}

@article{fcnnet,
	author    = {Hongming Li and
	Yong Fan},
	title     = {Non-rigid image registration using fully convolutional networks with
	deep self-supervision},
	journal   = {CoRR},
	volume    = {abs/1709.00799},
	year      = {2017},
	url       = {http://arxiv.org/abs/1709.00799},
	archivePrefix = {arXiv},
	eprint    = {1709.00799},
	timestamp = {Sun, 24 Dec 2017 01:07:47 +0100},
	biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1709-00799},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{Wu:2016, 
	author={G. Wu and M. Kim and Q. Wang and B. C. Munsell and D. Shen}, 
	journal={IEEE Transactions on Biomedical Engineering}, 
	title={Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning}, 
	year={2016}, 
	volume={63}, 
	number={7}, 
	pages={1505-1516}, 
	keywords={Biomedical imaging;Feature extraction;Image registration;Machine learning;Three-dimensional displays;Unsupervised learning;Deep learning;Deformable image registration;deep learning;deformable image registration;hierarchical feature representation;Algorithms;Brain;Humans;Image Processing, Computer-Assisted;Magnetic Resonance Imaging;Unsupervised Machine Learning}, 
	doi={10.1109/TBME.2015.2496253}, 
	ISSN={0018-9294}, 
	month={July}
}

@article{Unflow,
	author    = {Simon Meister and
	Junhwa Hur and
	Stefan Roth},
	title     = {UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional
	Census Loss},
	journal   = {CoRR},
	volume    = {abs/1711.07837},
	year      = {2017},
	url       = {http://arxiv.org/abs/1711.07837},
	archivePrefix = {arXiv},
	eprint    = {1711.07837},
	timestamp = {Thu, 14 Dec 2017 17:11:48 +0100},
	biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1711-07837},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{E2E,
	author    = {Siyuan Shan and
	Xiaoqing Guo and
	Wen Yan and
	Eric I{-}Chao Chang and
	Yubo Fan and
	Yan Xu},
	title     = {Unsupervised End-to-end Learning for Deformable Medical Image Registration},
	journal   = {CoRR},
	volume    = {abs/1711.08608},
	year      = {2017},
	url       = {http://arxiv.org/abs/1711.08608},
	archivePrefix = {arXiv},
	eprint    = {1711.08608},
	timestamp = {Sun, 03 Dec 2017 12:38:15 +0100},
	biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1711-08608},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{SpyNet2,
	author    = {Armand Zampieri and
	Guillaume Charpiat and
	Yuliya Tarabalka},
	title     = {Coarse to fine non-rigid registration: a chain of scale-specific neural
	networks for multimodal image alignment with application to remote
	sensing},
	journal   = {CoRR},
	volume    = {abs/1802.09816},
	year      = {2018},
	url       = {http://arxiv.org/abs/1802.09816},
	archivePrefix = {arXiv},
	eprint    = {1802.09816},
	timestamp = {Fri, 02 Mar 2018 13:46:22 +0100},
	biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1802-09816},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}`

@article{FlowNet2,
	author    = {Eddy Ilg and
	Nikolaus Mayer and
	Tonmoy Saikia and
	Margret Keuper and
	Alexey Dosovitskiy and
	Thomas Brox},
	title     = {FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks},
	journal   = {CoRR},
	volume    = {abs/1612.01925},
	year      = {2016},
	url       = {http://arxiv.org/abs/1612.01925},
	archivePrefix = {arXiv},
	eprint    = {1612.01925},
	timestamp = {Wed, 07 Jun 2017 14:41:53 +0200},
	biburl    = {https://dblp.org/rec/bib/journals/corr/IlgMSKDB16},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{WarpNet,
	author    = {Angjoo Kanazawa and
	David W. Jacobs and
	Manmohan Chandraker},
	title     = {WarpNet: Weakly Supervised Matching for Single-view Reconstruction},
	journal   = {CoRR},
	volume    = {abs/1604.05592},
	year      = {2016},
	url       = {http://arxiv.org/abs/1604.05592},
	archivePrefix = {arXiv},
	eprint    = {1604.05592},
	timestamp = {Wed, 07 Jun 2017 14:41:08 +0200},
	biburl    = {https://dblp.org/rec/bib/journals/corr/KanazawaJC16},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{saalfeld:2012,
	Author = {Saalfeld, Stephan and Fetter, Richard and Cardona, Albert and Tomancak, Pavel},
	Date = {2012/06/10/online},
	Date-Added = {2018-03-02 23:30:16 +0000},
	Date-Modified = {2018-03-02 23:30:16 +0000},
	Day = {10},
	Journal = {Nature Methods},
	L3 = {10.1038/nmeth.2072; https://www.nature.com/articles/nmeth.2072#supplementary-information},
	Month = {06},
	Pages = {717 EP  -},
	Publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. SN  -},
	Title = {Elastic volume reconstruction from series of ultra-thin microscopy sections},
	Ty = {JOUR},
	Url = {http://dx.doi.org/10.1038/nmeth.2072},
	Volume = {9},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nmeth.2072}}

@inproceedings{luo2016efficient,
  title={Efficient deep learning for stereo matching},
  author={Luo, Wenjie and Schwing, Alexander G and Urtasun, Raquel},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5695--5703},
  year={2016}
}
@inproceedings{revaud2015epicflow,
  title={Epicflow: Edge-preserving interpolation of correspondences for optical flow},
  author={Revaud, Jerome and Weinzaepfel, Philippe and Harchaoui, Zaid and Schmid, Cordelia},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1164--1172},
  year={2015}
}

@inproceedings{weinzaepfel2013deepflow,
  title={DeepFlow: Large displacement optical flow with deep matching},
  author={Weinzaepfel, Philippe and Revaud, Jerome and Harchaoui, Zaid and Schmid, Cordelia},
  booktitle={Computer Vision (ICCV), 2013 IEEE International Conference on},
  pages={1385--1392},
  year={2013},
  organization={IEEE}
}

@inproceedings{yoo2015multi,
  title={Multi-scale pyramid pooling for deep convolutional representation},
  author={Yoo, Donggeun and Park, Sunggyun and Lee, Joon-Young and Kweon, In So},
  booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 IEEE Conference on},
  pages={71--80},
  year={2015},
  organization={IEEE}
}