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We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT.
Robust wide baseline stereo from maximally stable extrema regions
Jiri Matas, Ondrej Chum, Martin Urban, and Tomas Pajdla · 2002
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Video google: A text retrieval approach to object matching in videos
Josef Sivic and Andrew Zisserman · 2003
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The general inefficiency of batch training for gradient descent learning
D. Randall Wilson and Tony R. Martinez · 2003
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Distinctive image features from scale-invariant keypoints
David G. Lowe · 2004
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Scale & affine invariant interest point detectors
Krystian Mikolajczyk and Cordelia Schmid · 2004
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A comparison of affine region detectors
Krystian Mikolajczyk, Tinne Tuytelaars, Cordelia Schmid, Andrew Zisserman, Jiri Matas, Frederik Schaffalitzky, Timor Kadir, and Luc Van Gool · 2005
Earlier work this paper cites.
Automatic panoramic image stitching using invariant features
Matthew Brown and David G. Lowe · 2007
Earlier work this paper cites.
Object retrieval with large vocabularies and fast spatial matching
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2007
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Registration of challenging image pairs: Initialization, estimation, and decision
Gehua Yang, Charles V Stewart, Michal Sofka, and Chia-Ling Tsai · 2007
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Lost in quantization: Improving particular object retrieval in large scale image databases
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2008
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Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja and David G. Lowe · 2009
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Efficient representation of local geometry for large scale object retrieval
Michal Perdoch, Ondrej Chum, and Jiri Matas · 2009
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On the burstiness of visual elements
Herve Jegou, Matthijs Douze, and Cordelia Schmid · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
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ORB: An efficient alternative to SIFT or SURF
Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski · 2011
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Edge foci interest points
C. Lawrence Zitnick and Krishnan Ramnath · 2011
Cited alongside, same era.
Three things everyone should know to improve object retrieval
Relja Arandjelovic and Andrew Zisserman · 2012
Cited alongside, same era.
Descriptor learning using convex optimisation
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2012
Cited alongside, same era.
Image matching using local symmetry features
Daniel C. Hauagge and Noah Snavely · 2012
Cited alongside, same era.
Learning vocabularies over a fine quantization
Andrej Mikulik, Michal Perdoch, Ondřej Chum, and Jiří Matas · 2013
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy · 2015
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Mods: Fast and robust method for two-view matching
Dmytro Mishkin, Jiri Matas, and Michal Perdoch · 2015
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Location recognition over large time lags
Basura Fernando, Tatiana Tommasi, and Tinne Tuytelaars · 2015
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CNN image retrieval learns from BoW: Unsupervised fine-tuning with hard examples
Filip Radenovic, Giorgos Tolias, and Ondrej Chum · 2016
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Structure-from-motion revisited
Johannes L. Schonberger and Jan-Michael Frahm · 2016
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Cited alongside, same era.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2014
Cited alongside, same era.
Visual query expansion with or without geometry: refining local descriptors by feature aggregation
Giorgos Tolias and Herve Jegou · 2014
Cited alongside, same era.
From single image query to detailed 3D reconstruction
Johannes L. Schonberger, Filip Radenovic, Ondrej Chum, and Jan-Michael Frahm · 2015
Cited alongside, same era.
Posenet: A convolutional network for real-time 6-DOF camera relocalization
Alex Kendall, Matthew Grimes, and Roberto Cipolla · 2015
Cited alongside, same era.
Matchnet: Unifying feature and metric learning for patch-based matching
Xufeng Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg · 2015
Cited alongside, same era.
Learning to compare image patches via convolutional neural networks
Sergey Zagoruyko and Nikos Komodakis · 2015
Cited alongside, same era.
Universal correspondence network
Christopher B. Choy, JunYoung Gwak, Silvio Savarese, and Manmohan Chandraker · 2016
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LIFT: Learned invariant feature transform
Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua · 2016
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Learning local feature descriptors with triplets and shallow convolutional neural networks
Vassileios Balntas, Edgar Riba, Daniel Ponsa, and Krystian Mikolajczyk · 2016
Later among the works it cites.
Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions
Vijay Kumar B. G., Gustavo Carneiro, and Ian Reid · 2016
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HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk · 2017
Closest in time.
Comparative evaluation of hand-crafted and learned local features
Johannes L. Schonberger, Hans Hardmeier, Torsten Sattler, and Marc Pollefeys · 2017
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L2-Net: Deep learning of discriminative patch descriptor in euclidean space
Bin Fan Yurun Tian and Fuchao Wu · 2017
Closest in time.
Multiple-kernel local-patch descriptor
Arun Mukundan, Giorgos Tolias, and Ondrej Chum · 2017
Closest in time.
A Large Dataset for Improving Patch Matching
R. Mitra, N. Doiphode, U. Gautam, S. Narayan, S. Ahmed, S. Chandran, and A. Jain · 2018
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