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Establishing dense correspondences between a pair of images is an important and general problem.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
Martin A. Fischler and Robert C. Bolles · 1981
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”determining optical flow”: A retrospective
Berthold K. P. Horn and Brian G. Schunck · 1993
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Performance of optical flow techniques
J. L. Barron, D. J. Fleet, and S. S. Beauchemin · 1994
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Best practices for convolutional neural networks applied to visual document analysis
Patrice Y. Simard, Dave Steinkraus, and John C. Platt · 2003
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Distinctive image features from scale-invariant keypoints
David G. Lowe · 2004
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An efficient solution to the five-point relative pose problem
David Nistér · 2004
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An adaptive confidence measure for optical flows based on linear subspace projections
Claudia Kondermann, Daniel Kondermann, Bernd Jähne, and Christoph S. Garbe · 2007
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A statistical confidence measure for optical flows
Claudia Kondermann, Rudolf Mester, and Christoph S. Garbe · 2008
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A database and evaluation methodology for optical flow
Simon Baker, Daniel Scharstein, J. P. Lewis, Stefan Roth, Michael J. Black, and Richard Szeliski · 2011
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Non-rigid dense correspondence with applications for image enhancement
Yoav HaCohen, Eli Shechtman, Dan B. Goldman, and Dani Lischinski · 2011
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Bootstrap optical flow confidence and uncertainty measure
Jan Kybic and Claudia Nieuwenhuis · 2011
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SIFT flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2011
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Data-driven visual similarity for cross-domain image matching
Abhinav Shrivastava, Tomasz Malisiewicz, Abhinav Gupta, and Alexei A. Efros · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Image retrieval for image-based localization revisited
Torsten Sattler, Tobias Weyand, Bastian Leibe, and Leif Kobbelt · 2012
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Learning a confidence measure for optical flow
Oisin Mac Aodha, Ahmad Humayun, M. Pollefeys, and G. Brostow · 2013
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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A quantitative analysis of current practices in optical flow estimation and the principles behind them
Deqing Sun, Stefan Roth, and Michael J. Black · 2014
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Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset)
Jared Heinly, Johannes Lutz Schönberger, Enrique Dunn, and Jan-Michael Frahm · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Epicflow: Edge-preserving interpolation of correspondences for optical flow
Jérôme Revaud, Philippe Weinzaepfel, Zaïd Harchaoui, and Cordelia Schmid · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Structure-from-motion revisited
Johannes L. Schönberger and Jan-Michael Frahm · 2016
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YFCC100M: the new data in multimedia research
Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Dslr-quality photos on mobile devices with deep convolutional networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Semantic attribute matching networks
Seungryong Kim, Dongbo Min, Somi Jeong, Sunok Kim, Sangryul Jeon, and Kwanghoon Sohn · 2019
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A cross-season correspondence dataset for robust semantic segmentation
Måns Larsson, Erik Stenborg, Lars Hammarstrand, Marc Pollefeys, Torsten Sattler, and Fredrik Kahl · 2019
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DGC-Net: Dense geometric correspondence network
Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, and Juho Kannala · 2019
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Glampoints: Greedily learned accurate match points
Prune Truong, Stefanos Apostolopoulos, Agata Mosinska, Samuel Stucky, Carlos Ciller, and Sandro De Zanet · 2019
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Handheld multi-frame super-resolution
Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar · 2019
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Volumetric correspondence networks for optical flow
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Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Visual attribute transfer through deep image analogy
Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, and Sing Bing Kang · 2017
Cited alongside, same era.
1 Year, 1000km: The Oxford RobotCar Dataset
Will Maddern, Geoff Pascoe, Chris Linegar, and Paul Newman · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Convolutional neural network architecture for geometric matching
Ignacio Rocco, Relja Arandjelovic, and Josef Sivic · 2017
Cited alongside, same era.
A multi-view stereo benchmark with high-resolution images and multi-camera videos
Thomas Schöps, Johannes L. Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger · 2017
Cited alongside, same era.
Gengshan Yang and Deva Ramanan · 2019
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Hierarchical discrete distribution decomposition for match density estimation
Zhichao Yin, Trevor Darrell, and Fisher Yu · 2019
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Learning two-view correspondences and geometry using order-aware network
Jiahui Zhang, Dawei Sun, Zixin Luo, Anbang Yao, Lei Zhou, Tianwei Shen, Yurong Chen, Hongen Liao, and Long Quan · 2019
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Semantic understanding of scenes through the ADE20K dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
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Albumentations: Fast and flexible image augmentations
Alexander Buslaev, Vladimir I. Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A. Kalinin · 2020
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Probabilistic regression for visual tracking
Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
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Energy-based models for deep probabilistic regression
Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, and Thomas B. Schön · 2020
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A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy · 2020
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Optical flow estimation in the deep learning age
Junhwa Hur and S. Roth · 2020
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Robust instance tracking via uncertainty flow
Jianing Qian, Junyu Nan, Siddharth Ancha, Brian Okorn, and David Held · 2020
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Efficient neighbourhood consensus networks via submanifold sparse convolutions
I. Rocco, R. Arandjelović, and J. Sivic · 2020
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Ransac-flow: generic two-stage image alignment
Xi Shen, François Darmon, Alexei A Efros, and Mathieu Aubry · 2020
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Yichen Shen, Zhilu Zhang, Mert R. Sabuncu, and Lin Sun · 2020
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RAFT: recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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GOCor: Bringing globally optimized correspondence volumes into your neural network
Prune Truong, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
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GLU-Net: Global-local universal network for dense flow and correspondences
Prune Truong, Martin Danelljan, and Radu Timofte · 2020
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Mixture dense regression for object detection and human pose estimation
Ali Varamesh and Tinne Tuytelaars · 2020
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Uncertainty depth estimation with gated images for 3d reconstruction
Stefanie Walz, Tobias Gruber, Werner Ritter, and Klaus Dietmayer · 2020
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D2D: learning to find good correspondences for image matching and manipulation
Olivia Wiles, Sébastien Ehrhardt, and Andrew Zisserman · 2020
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