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Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval.
Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi · 1994
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Learning precise timing with LSTM recurrent networks
F. A. Gers, N. N. Schraudolph, and J. Schmidhuber · 2002
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A combined LSTM-RNN - HMM - approach for meeting event segmentation and recognition
S. Reiter, B. Schuller, and G. Rigoll · 2006
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A duality based approach for realtime tv-l1 optical flow
C. Zach, T. Pock, and H. Bischof · 2007
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Phoneme recognition in TIMIT with BLSTM-CTC
S. Fernández, A. Graves, and J. Schmidhuber · 2008
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Offline handwriting recognition with multidimensional recurrent neural networks
A. Graves and J. Schmidhuber · 2008
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Learning realistic human actions from movies
I. Laptev, M. Marszałek, C. Schmid, and B. Rozenfeld · 2008
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A novel connectionist system for unconstrained handwriting recognition
A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber · 2009
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Evaluation of local spatio-temporal features for action recognition
H. Wang, M. M. Ullah, A. Kläser, I. Laptev, and C. Schmid · 2009
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Action classification in soccer videos with long short-term memory recurrent neural networks
M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt · 2010
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A theoretical analysis of feature pooling in visual recognition
Y.-L. Boureau, J. Ponce, and Y. Lecun · 2010
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Sequential Deep Learning for Human Action Recognition
M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt · 2011
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HMDB: a large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
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Action recognition by dense trajectories
H. Wang, A. Klaser, C. Schmid, and C.-L. Liu · 2011
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
3D convolutional neural networks for human action recognition
S. Ji, W. Xu, M. Yang, and K. Yu · 2013
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Action Recognition with Improved Trajectories
H. Wang and C. Schmid · 2013
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LSTM-modeling of continuous emotions in an audiovisual affect recognition framework
M. Wöllmer, M. Kaiser, F. Eyben, B. Schuller, and G. Rigoll · 2013
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Towards end-to-end speech recognition with recurrent neural networks
A. Graves and N. Jaitly · 2014
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
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Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
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Cited alongside, same era.
UCF101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. R. Zamir, and M. Shah · 2012
Cited alongside, same era.
Speech recognition with deep recurrent neural networks
A. Graves, A.-R. Mohamed, and G. E. Hinton · 2013
Cited alongside, same era.
Better exploiting motion for better action recognition
M. Jain, H. Jégou, and P. Bouthemy · 2013
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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W. Zaremba and I. Sutskever · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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