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In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications.
Extraction of visual features for lipreading
I. Matthews, T. F. Cootes, J. A. Bangham, S. Cox, and R. Harvey · 2002
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Cuave: A new audio-visual database for multimodal human-computer interface research
E. K. Patterson, S. Gurbuz, Z. Tufekci, and J. N. Gowdy · 2002
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Canonical correlation analysis: An overview with application to learning methods
D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor · 2004
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Collecting complex activity datasets in highly rich networked sensor environments
D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. Förster, G. Tröster, P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha, et al · 2010
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Multimodal deep learning
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng · 2011
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Multimodal learning with deep boltzmann machines
N. Srivastava and R. R. Salakhutdinov · 2012
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Deep canonical correlation analysis
G. Andrew, R. Arora, J. Bilmes, and K. Livescu · 2013
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Multimodal fusion using dynamic hybrid models
M. R. Amer, B. Siddiquie, S. Khan, A. Divakaran, and H. Sawhney · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
K. Cho, B. van Merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Cited alongside, same era.
Multimodal neural language models
R. Kiros, R. Salakhutdinov, and R. Zemel · 2014
Cited alongside, same era.
Improved multimodal deep learning with variation of information
K. Sohn, W. Shang, and H. Lee · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Cited alongside, same era.
Correlational neural networks
S. Chandar, M. M. Khapra, H. Larochelle, and B. Ravindran · 2015
Cited alongside, same era.
Audiovisual fusion: Challenges and new approaches
A. K. Katsaggelos, S. Bahaadini, and R. Molina · 2015
Cited alongside, same era.
Unsupervised learning of video representations using lstms
N. Srivastava, E. Mansimov, and R. Salakhutdinov · 2015
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Listening with your eyes: Towards a practical visual speech recognition system using deep boltzmann machines
C. Sui, M. Bennamoun, and R. Togneri · 2015
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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 · 2015
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On deep multi-view representation learning
W. Wang, R. Arora, K. Livescu, and J. Bilmes · 2015
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Temporal multimodal learning in audiovisual speech recognition
D. Hu, X. Li, et al · 2016
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On-the-fly hand detection training with application in egocentric action recognition
J. Kumar, Q. Li, S. Kyal, E. Bernal, and R. Bala · 2015
Cited alongside, same era.
The av+ ec 2015 multimodal affect recognition challenge: Bridging across audio, video, and physiological data
F. Ringeval, B. Schuller, M. Valstar, S. Jaiswal, E. Marchi, D. Lalanne, R. Cowie, and M. Pantic · 2015
Cited alongside, same era.
Moddrop: adaptive multi-modal gesture recognition
N. Neverova, C. Wolf, G. Taylor, and F. Nebout · 2016
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Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition
F. J. Ordóñez and D. Roggen · 2016
Later among the works it cites.
Look, listen and learn-a multimodal lstm for speaker identification
J. Ren, Y. Hu, Y.-W. Tai, C. Wang, L. Xu, W. Sun, and Q. Yan · 2016
Later among the works it cites.