Deep Structured Learning for Facial Expression Intensity Estimation
暂无分享,去创建一个
Vladimir Pavlovic | Maja Pantic | Ognjen Rudovic | Robert Walecki | V. Pavlovic | M. Pantic | Ognjen Rudovic | Bjöern Schuller | B. Schuller | R. Walecki
[1] Ashish Kapoor,et al. Multimodal affect recognition in learning environments , 2005, ACM Multimedia.
[2] P. McCullagh. Analysis of Ordinal Categorical Data , 1985 .
[3] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[4] Jeffrey F. Cohn,et al. Painful data: The UNBC-McMaster shoulder pain expression archive database , 2011, Face and Gesture 2011.
[5] Guosheng Lin,et al. Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Shiguang Shan,et al. AU-aware Deep Networks for facial expression recognition , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[7] Francis Tuerlinckx,et al. Copula Functions for Residual Dependency , 2007 .
[8] Ping Liu,et al. Facial Expression Recognition via a Boosted Deep Belief Network , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[10] Vladimir Pavlovic,et al. Context-Sensitive Dynamic Ordinal Regression for Intensity Estimation of Facial Action Units , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Honggang Zhang,et al. Deep Region and Multi-label Learning for Facial Action Unit Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Karianto Leman,et al. Shadow optimization from structured deep edge detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Mohamed Chetouani,et al. Facial Action Unit intensity prediction via Hard Multi-Task Metric Learning for Kernel Regression , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[14] Frank D. Wood,et al. Characterizing neural dependencies with copula models , 2008, NIPS.
[15] Daniel S. Messinger,et al. A framework for automated measurement of the intensity of non-posed Facial Action Units , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[16] Rob Fergus,et al. Restoring an Image Taken through a Window Covered with Dirt or Rain , 2013, 2013 IEEE International Conference on Computer Vision.
[17] Katherine B. Martin,et al. Facial Action Coding System , 2015 .
[18] Xiaoou Tang,et al. Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.
[19] Lijun Yin,et al. FERA 2015 - second Facial Expression Recognition and Analysis challenge , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[20] Noah A. Smith,et al. An Exact Dual Decomposition Algorithm for Shallow Semantic Parsing with Constraints , 2012, *SEMEVAL.
[21] Qiang Ji,et al. A unified probabilistic framework for measuring the intensity of spontaneous facial action units , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[22] H. Emrah Tasli,et al. Deep learning based FACS Action Unit occurrence and intensity estimation , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[23] Stefanos Zafeiriou,et al. Markov Random Field Structures for Facial Action Unit Intensity Estimation , 2013, 2013 IEEE International Conference on Computer Vision Workshops.
[24] Xiaogang Wang,et al. Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Mohammad H. Mahoor,et al. DISFA: A Spontaneous Facial Action Intensity Database , 2013, IEEE Transactions on Affective Computing.
[26] Gang Hua,et al. Ordinal Regression with Multiple Output CNN for Age Estimation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Alan L. Yuille,et al. Learning Deep Structured Models , 2014, ICML.
[28] Sebastian Nowozin,et al. Structured Learning and Prediction in Computer Vision , 2011, Found. Trends Comput. Graph. Vis..
[29] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[30] Thomas S. Huang,et al. Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition? , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).
[31] Jian Sun,et al. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[32] Gwen Littlewort,et al. Automatic Recognition of Facial Actions in Spontaneous Expressions , 2006, J. Multim..
[33] Maja Pantic,et al. Latent trees for estimating intensity of Facial Action Units , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Vladimir Pavlovic,et al. Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] C. Genest. Frank's family of bivariate distributions , 1987 .
[36] Rob Fergus,et al. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[37] Simon Lucey,et al. Investigating Spontaneous Facial Action Recognition through AAM Representations of the Face , 2007 .
[38] H. Friedl. Econometric Analysis of Count Data , 2002 .
[39] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[40] T. Louis,et al. Inferences on the association parameter in copula models for bivariate survival data. , 1995, Biometrics.
[41] Andrew McCallum,et al. Piecewise pseudolikelihood for efficient training of conditional random fields , 2007, ICML '07.
[42] Dumitru Erhan,et al. Deep Neural Networks for Object Detection , 2013, NIPS.
[43] Vladimir Pavlovic,et al. Structured Output Ordinal Regression for Dynamic Facial Emotion Intensity Prediction , 2010, ECCV.