EmoNets: Multimodal deep learning approaches for emotion recognition in video
暂无分享,去创建一个
Christopher Joseph Pal | Yoshua Bengio | Yann Dauphin | Aaron C. Courville | Roland Memisevic | Samira Ebrahimi Kahou | Vincent Michalski | David Warde-Farley | Kishore Reddy Konda | Xavier Bouthillier | Çaglar Gülçehre | Pascal Vincent | Mehdi Mirza | Nicolas Boulanger-Lewandowski | Pascal Lamblin | Pierre Froumenty | Sébastien Jean | Raul Chandias Ferrari | Yoshua Bengio | Pascal Lamblin | Pascal Vincent | Çaglar Gülçehre | M. Mirza | Nicolas Boulanger-Lewandowski | Xavier Bouthillier | Yann Dauphin | S. Kahou | Sébastien Jean | R. Memisevic | Vincent Michalski | C. Pal | David Warde-Farley | Pierre Froumenty | K. Konda | Mehdi Mirza | Y. Dauphin
[1] E H Adelson,et al. Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.
[2] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[3] Sébastien Marcel,et al. Lighting Normalization Algorithms for Face Verification , 2005 .
[4] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[5] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[6] Vitomir Struc,et al. Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition , 2009, Informatica.
[7] Cordelia Schmid,et al. Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.
[8] Yann LeCun,et al. Convolutional Learning of Spatio-temporal Features , 2010, ECCV.
[9] Razvan Pascanu,et al. Theano: A CPU and GPU Math Compiler in Python , 2010, SciPy.
[10] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[11] Quoc V. Le,et al. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis , 2011, CVPR 2011.
[12] Honglak Lee,et al. An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.
[13] Vitomir Struc,et al. Photometric Normalization Techniques for Illumination Invariance , 2011 .
[14] Douglas Eck,et al. Temporal Pooling and Multiscale Learning for Automatic Annotation and Ranking of Music Audio , 2011, ISMIR.
[15] Tamás D. Gedeon,et al. Collecting Large, Richly Annotated Facial-Expression Databases from Movies , 2012, IEEE MultiMedia.
[16] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[17] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[18] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[19] Razvan Pascanu,et al. Theano: new features and speed improvements , 2012, ArXiv.
[20] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[21] Deva Ramanan,et al. Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Gwen Littlewort,et al. Multiple kernel learning for emotion recognition in the wild , 2013, ICMI '13.
[23] Tara N. Sainath,et al. Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[24] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[25] Hazim Kemal Ekenel,et al. Why is facial expression analysis in the wild challenging? , 2013, EmotiW '13.
[26] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[27] Abhinav Dhall,et al. Emotion recognition in the wild challenge 2013 , 2013, ICMI '13.
[28] Shiguang Shan,et al. Partial least squares regression on grassmannian manifold for emotion recognition , 2013, ICMI '13.
[29] Razvan Pascanu,et al. Combining modality specific deep neural networks for emotion recognition in video , 2013, ICMI '13.
[30] Zheru Chi,et al. Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning , 2014, ICMI.
[31] Phil Blunsom,et al. A Convolutional Neural Network for Modelling Sentences , 2014, ACL.
[32] Nicu Sebe,et al. Temporal Dropout of Changes Approach to Convolutional Learning of Spatio-Temporal Features , 2014, ACM Multimedia.
[33] Roland Memisevic,et al. The role of spatio-temporal synchrony in the encoding of motion , 2013, ICLR.
[34] Shiguang Shan,et al. Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild , 2014, ICMI.
[35] Tamás D. Gedeon,et al. Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol , 2014, ICMI.
[36] Christopher Joseph Pal,et al. Facial Expression Analysis Based on High Dimensional Binary Features , 2014, ECCV Workshops.
[37] Ying Chen,et al. Combining Multimodal Features with Hierarchical Classifier Fusion for Emotion Recognition in the Wild , 2014, ICMI.
[38] R. Goecke,et al. Emotion recognition in the wild challenge 2016 , 2016, ICMI.
[39] Christian Wolf,et al. ModDrop: Adaptive Multi-Modal Gesture Recognition , 2014, IEEE Trans. Pattern Anal. Mach. Intell..