Fetching the paper…
Reading the bibliography…
The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations.
Evaluating derivatives: Principles and techniques of algorithmic differentiation
A. Griewank and A. Walther · 2008
Earlier work this paper cites.
Visual object tracking using adaptive correlation filters
D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui · 2010
Earlier work this paper cites.
Deconvolutional networks
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus · 2010
Earlier work this paper cites.
Zero-aliasing correlation filters
J. A. Fernandez and B. Vijayakumar · 2013
Earlier work this paper cites.
Maximum margin correlation filter: A new approach for localization and classification
A. Rodriguez, V. N. Boddeti, B. V. K. V. Kumar, and A. Mahalanobis · 2013
Earlier work this paper cites.
Online object tracking: A benchmark
Y. Wu, J. Lim, and M.-H. Yang · 2013
Earlier work this paper cites.
Accurate scale estimation for robust visual tracking
M. Danelljan, G. Häger, F. Khan, and M. Felsberg · 2014
Earlier work this paper cites.
A scale adaptive kernel correlation filter tracker with feature integration
Y. Li and J. Zhu · 2014
Earlier work this paper cites.
Learning detectors quickly with stationary statistics
J. Valmadre, S. Sridharan, and S. Lucey · 2014
Earlier work this paper cites.
Convolutional features for correlation filter based visual tracking
M. Danelljan, G. Hager, F. Shahbaz Khan, and M. Felsberg · 2015
Earlier work this paper cites.
Learning spatially regularized correlation filters for visual tracking
M. Danelljan, G. Hager, F. Shahbaz Khan, and M. Felsberg · 2015
Earlier work this paper cites.
High-speed tracking with kernelized correlation filters
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista · 2015
Earlier work this paper cites.
Matrix backpropagation for deep networks with structured layers
C. Ionescu, O. Vantzos, and C. Sminchisescu · 2015
Earlier work this paper cites.
Correlation filters with limited boundaries
H. Kiani Galoogahi, T. Sim, and S. Lucey · 2015
Cited alongside, same era.
Encoding color information for visual tracking: Algorithms and benchmark
P. Liang, E. Blasch, and H. Ling · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Hierarchical convolutional features for visual tracking
C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang · 2015
Cited alongside, same era.
Long-term correlation tracking
C. Ma, X. Yang, C. Zhang, and M.-H. Yang · 2015
Cited alongside, same era.
Gradient-based hyperparameter optimization through reversible learning
D. Maclaurin, D. Duvenaud, and R. P. Adams · 2015
Cited alongside, same era.
Fully-convolutional Siamese networks for object tracking
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S. Torr · 2016
Later among the works it cites.
Once for all: A two-flow convolutional neural network for visual tracking
K. Chen and W. Tao · 2016
Later among the works it cites.
Beyond correlation filters: Learning continuous convolution operators for visual tracking
M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg · 2016
Later among the works it cites.
S. Gould, B. Fernando, A. Cherian, P. Anderson, R. S. Cruz, and E. Guo · 2016
Later among the works it cites.
Learning to track at 100 fps with deep regression networks
D. Held, S. Thrun, and S. Savarese · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Transferring rich feature hierarchies for robust visual tracking
N. Wang, S. Li, A. Gupta, and D.-Y. Yeung · 2015
Cited alongside, same era.
Object tracking benchmark
Y. Wu, J. Lim, and M.-H. Yang · 2015
Cited alongside, same era.
Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. S. Torr · 2015
Cited alongside, same era.
Learning feed-forward one-shot learners
L. Bertinetto, J. F. Henriques, J. Valmadre, P. H. S. Torr, and A. Vedaldi · 2016
Cited alongside, same era.
Staple: Complementary learners for real-time tracking
L. Bertinetto, J. Valmadre, S. Golodetz, O. Miksik, and P. H. S. Torr · 2016
Cited alongside, same era.
The Visual Object Tracking VOT2016 challenge results
M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pflugfelder, L. Čehovin, T. Vojír̃, G. Häger, A. Lukežič, G. Fernández, et al · 2016
Later among the works it cites.
Learning by tracking: Siamese CNN for robust target association
L. Leal-Taixé, C. Canton-Ferrer, and K. Schindler · 2016
Later among the works it cites.
Differentiation of the Cholesky decomposition
I. Murray · 2016
Later among the works it cites.
Learning multi-domain convolutional neural networks for visual tracking
H. Nam and B. Han · 2016
Later among the works it cites.
Siamese instance search for tracking
R. Tao, E. Gavves, and A. W. M. Smeulders · 2016
Later among the works it cites.
Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
Later among the works it cites.
Deep learning of appearance models for online object tracking
M. Zhai, M. J. Roshtkhari, and G. Mori · 2016
Later among the works it cites.