Built on

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning , vol. 28, no. 1, pp. 41–75, 1997
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
C. Schüldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local svm approach,” in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on , vol. 3. IEEE, 2004, pp. 32–36
2004
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in International Conference on Computer Vision & Pattern Recognition , C. Schmid, S. Soatto, and C. Tomasi, Eds., vol. 2, INRIA Rhône-Alpes, ZIRST-655, av. de l’Europe, Montbonnot-38334, June 2005, pp. 886–893. [Online]. Available: http://lear.inrialpes.fr/pubs/2005/DT05
2005
Earlier work this paper cites.
A. Ess, B. Leibe, K. Schindler, , and L. van Gool, “A mobile vision system for robust multi-person tracking,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR’08) . IEEE Press, June 2008
2008
Earlier work this paper cites.
I. Sutskever, G. E. Hinton, and G. W. Taylor, “The recurrent temporal restricted boltzmann machine,” in Advances in Neural Information Processing Systems , 2009, pp. 1601–1608
2009
Earlier work this paper cites.
H. Larochelle and G. E. Hinton, “Learning to combine foveal glimpses with a third-order boltzmann machine,” in Advances in neural information processing systems , 2010, pp. 1243–1251
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on Machine Learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, “Theano: a cpu and gpu math expression compiler,” in Proceedings of the Python for scientific computing conference (SciPy) , vol. 4. Austin, TX, 2010, p. 3
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International journal of computer vision , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
M. Denil, L. Bazzani, H. Larochelle, and N. de Freitas, “Learning where to attend with deep architectures for image tracking,” CoRR , vol. abs/1109.3737, 2011. [Online]. Available: http://arxiv.org/abs/1109.3737
Original
2011
Earlier work this paper cites.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” arXiv preprint arXiv:1211.5063 , 2012
Original
2012
Earlier work this paper cites.

Similar

F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio, “Theano: new features and speed improvements,” arXiv preprint arXiv:1211.5590 , 2012
Original
2012
Cited alongside, same era.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580 , 2012
Original
2012
Cited alongside, same era.
Z. Jiang, Z. Lin, and L. S. Davis, “Recognizing human actions by learning and matching shape-motion prototype trees,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol. 34, no. 3, pp. 533–547, 2012
2012
Cited alongside, same era.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
2012
Cited alongside, same era.

Then

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alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2015

RATM: Recurrent Attentive Tracking Model

Kahou, Samira Ebrahimi, Michalski, Vincent, Memisevic, Roland

Understand

We present an attention-based modular neural framework for computer vision.

The framework uses a soft attention mechanism allowing models to be trained with gradient descent.
It consists of three modules: a recurrent attention module controlling where to look in an image or video frame, a feature-extraction module providing a representation of what is seen, and an objective module formalizing why the model learns its attentive behavior.
The attention module allows the model to focus computation on task-related information in the input.

Built on

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning , vol. 28, no. 1, pp. 41–75, 1997
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
C. Schüldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local svm approach,” in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on , vol. 3. IEEE, 2004, pp. 32–36
2004
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in International Conference on Computer Vision & Pattern Recognition , C. Schmid, S. Soatto, and C. Tomasi, Eds., vol. 2, INRIA Rhône-Alpes, ZIRST-655, av. de l’Europe, Montbonnot-38334, June 2005, pp. 886–893. [Online]. Available: http://lear.inrialpes.fr/pubs/2005/DT05
2005
Earlier work this paper cites.
A. Ess, B. Leibe, K. Schindler, , and L. van Gool, “A mobile vision system for robust multi-person tracking,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR’08) . IEEE Press, June 2008
2008
Earlier work this paper cites.
I. Sutskever, G. E. Hinton, and G. W. Taylor, “The recurrent temporal restricted boltzmann machine,” in Advances in Neural Information Processing Systems , 2009, pp. 1601–1608
2009
Earlier work this paper cites.
H. Larochelle and G. E. Hinton, “Learning to combine foveal glimpses with a third-order boltzmann machine,” in Advances in neural information processing systems , 2010, pp. 1243–1251
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on Machine Learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, “Theano: a cpu and gpu math expression compiler,” in Proceedings of the Python for scientific computing conference (SciPy) , vol. 4. Austin, TX, 2010, p. 3
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International journal of computer vision , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
M. Denil, L. Bazzani, H. Larochelle, and N. de Freitas, “Learning where to attend with deep architectures for image tracking,” CoRR , vol. abs/1109.3737, 2011. [Online]. Available: http://arxiv.org/abs/1109.3737
Original
2011
Earlier work this paper cites.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” arXiv preprint arXiv:1211.5063 , 2012
Original
2012
Earlier work this paper cites.

Similar

F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio, “Theano: new features and speed improvements,” arXiv preprint arXiv:1211.5590 , 2012
Original
2012
Cited alongside, same era.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580 , 2012
Original
2012
Cited alongside, same era.
Z. Jiang, Z. Lin, and L. S. Davis, “Recognizing human actions by learning and matching shape-motion prototype trees,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol. 34, no. 3, pp. 533–547, 2012
2012
Cited alongside, same era.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
2012
Cited alongside, same era.

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

N. Wang and D.-Y. Yeung, “Learning a deep compact image representation for visual tracking,” in Advances in neural information processing systems , 2013, pp. 809–817

2013

Cited alongside, same era.

A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on . IEEE, 2013, pp. 6645–6649

2013

Cited alongside, same era.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014

Original

2014

Cited alongside, same era.

V. Mnih, N. Heess, A. Graves et al. , “Recurrent models of visual attention,” in Advances in Neural Information Processing Systems , 2014, pp. 2204–2212

2014

Cited alongside, same era.

M. Ranzato, “On learning where to look,” arXiv preprint arXiv:1405.5488 , 2014

Original

2014

Cited alongside, same era.

P. Sermanet, A. Frome, and E. Real, “Attention for fine-grained categorization,” arXiv preprint arXiv:1412.7054 , 2014

Original

2014

Cited alongside, same era.

A. W. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, “Visual tracking: An experimental survey,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol. 36, no. 7, pp. 1442–1468, 2014

2014

Cited alongside, same era.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112

2014

Cited alongside, same era.

S. K. Sønderby, C. K. Sønderby, H. Nielsen, and O. Winther, “Convolutional lstm networks for subcellular localization of proteins,” in Algorithms for computational biology . Springer, 2015, pp. 68–80

2015

Closest in time.

H. Nam and B. Han, “Learning multi-domain convolutional neural networks for visual tracking,” CoRR , vol. abs/1510.07945, 2015

Original

2015

Closest in time.

N. Srivastava, E. Mansimov, and R. Salakhutdinov, “Unsupervised learning of video representations using lstms,” arXiv preprint arXiv:1502.04681 , 2015

Original

2015

Closest in time.

Q. V. Le, N. Jaitly, and G. E. Hinton, “A simple way to initialize recurrent networks of rectified linear units,” arXiv preprint arXiv:1504.00941 , 2015

Original

2015

Closest in time.

S. Ebrahimi Kahou, V. Michalski, K. Konda, R. Memisevic, and C. Pal, “Recurrent neural networks for emotion recognition in video,” in Proceedings of the 2015 ACM on International Conference on Multimodal Interaction , ser. ICMI ’15. New York, NY, USA: ACM, 2015, pp. 467–474. [Online]. Available: http://doi.acm.org/10.1145/2818346.2830596

2015

Closest in time.

Y. Wu, J. Lim, and M.-H. Yang, “Object tracking benchmark,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol. 37, no. 9, pp. 1834–1848, 2015

2015

Closest in time.

S. K. Sønderby, C. K. Sønderby, L. Maaløe, and O. Winther, “Recurrent spatial transformer networks,” arXiv preprint arXiv:1509.05329 , 2015

Original

2015

Closest in time.

M. Jaderberg, K. Simonyan, A. Zisserman et al. , “Spatial transformer networks,” in Advances in Neural Information Processing Systems , 2015, pp. 2008–2016

2016

Closest in time.