ModDrop: Adaptive Multi-Modal Gesture Recognition
暂无分享,去创建一个
[1] N. Neverova. Deep learning for human motion analysis , 2016 .
[2] Sergio Escalera,et al. Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D , 2014, Pattern Recognit. Lett..
[3] Jun Wang,et al. Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification , 2014, ACM Multimedia.
[4] Christian Wolf,et al. Hand Segmentation with Structured Convolutional Learning , 2014, ACCV.
[5] Jonathan Tompson,et al. MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation , 2014, ACCV.
[6] Ken Perlin,et al. Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , 2014, ACM Trans. Graph..
[7] Christian Wolf,et al. Multi-scale Deep Learning for Gesture Detection and Localization , 2014, ECCV Workshops.
[8] Radu Horaud,et al. Continuous Gesture Recognition from Articulated Poses , 2014, ECCV Workshops.
[9] Di Wu,et al. Multi-modality Gesture Detection and Recognition with Un-supervision, Randomization and Discrimination , 2014, ECCV Workshops.
[10] Ju Yong Chang. Nonparametric Gesture Labeling from Multi-modal Data , 2014, ECCV Workshops.
[11] Ling Shao,et al. Deep Dynamic Neural Networks for Gesture Segmentation and Recognition , 2014, ECCV Workshops.
[12] Benjamin Schrauwen,et al. Sign Language Recognition Using Convolutional Neural Networks , 2014, ECCV Workshops.
[13] Sergio Escalera,et al. ChaLearn Looking at People Challenge 2014: Dataset and Results , 2014, ECCV Workshops.
[14] Lale Akarun,et al. Gesture Recognition Using Template Based Random Forest Classifiers , 2014, ECCV Workshops.
[15] Limin Wang,et al. Action and Gesture Temporal Spotting with Super Vector Representation , 2014, ECCV Workshops.
[16] Camille Monnier,et al. A Multi-scale Boosted Detector for Efficient and Robust Gesture Recognition , 2014, ECCV Workshops.
[17] Tae-Kyun Kim,et al. Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Fei-Fei Li,et al. Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[20] Sanja Fidler,et al. Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[21] Ming Yang,et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Chen Qian,et al. Realtime and Robust Hand Tracking from Depth , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Pierre Baldi,et al. The dropout learning algorithm , 2014, Artif. Intell..
[24] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[25] Sergio Escalera,et al. Multi-modal gesture recognition challenge 2013: dataset and results , 2013, ICMI '13.
[26] Razvan Pascanu,et al. Combining modality specific deep neural networks for emotion recognition in video , 2013, ICMI '13.
[27] Wei-Yun Yau,et al. A multi-modal gesture recognition system using audio, video, and skeletal joint data , 2013, ICMI '13.
[28] Markus Koskela,et al. Online RGB-D gesture recognition with extreme learning machines , 2013, ICMI '13.
[29] Giulio Paci,et al. A Multi-scale Approach to Gesture Detection and Recognition , 2013, 2013 IEEE International Conference on Computer Vision Workshops.
[30] Cristian Sminchisescu,et al. The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[31] Tae-Kyun Kim,et al. Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests , 2013, 2013 IEEE International Conference on Computer Vision.
[32] Nicu Sebe,et al. Feature Weighting via Optimal Thresholding for Video Analysis , 2013, 2013 IEEE International Conference on Computer Vision.
[33] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Camille Couprie,et al. Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Dong Liu,et al. Sample-Specific Late Fusion for Visual Category Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[36] Christopher D. Manning,et al. Fast dropout training , 2013, ICML.
[37] Yi Li,et al. Beyond Physical Connections: Tree Models in Human Pose Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Cordelia Schmid,et al. Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.
[39] Jessica K. Hodgins,et al. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Yoshua Bengio,et al. Maxout Networks , 2013, ICML.
[41] Yann LeCun,et al. Indoor Semantic Segmentation using depth information , 2013, ICLR.
[42] Nitish Srivastava,et al. Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..
[43] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[44] Christian Wolf,et al. Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification , 2012, BMVC.
[45] Geoffrey E. Hinton,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[46] Dong Liu,et al. Robust late fusion with rank minimization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[47] Shuang Wu,et al. Multimodal feature fusion for robust event detection in web videos , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[48] Ying Wu,et al. Mining actionlet ensemble for action recognition with depth cameras , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[49] Lale Akarun,et al. Real time hand pose estimation using depth sensors , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).
[50] Bart Selman,et al. Unstructured human activity detection from RGBD images , 2011, 2012 IEEE International Conference on Robotics and Automation.
[51] Juhan Nam,et al. Multimodal Deep Learning , 2011, ICML.
[52] Andrew W. Fitzgibbon,et al. Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.
[53] Quoc V. Le,et al. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis , 2011, CVPR 2011.
[54] Ming Yang,et al. 3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] Cordelia Schmid,et al. Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.
[56] Sebastian Nowozin,et al. On feature combination for multiclass object classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[57] Luc Van Gool,et al. An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector , 2008, ECCV.
[58] Cordelia Schmid,et al. A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.
[59] Cordelia Schmid,et al. Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[60] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[61] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[62] Serge J. Belongie,et al. Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.
[63] Michael I. Jordan,et al. Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.
[64] Luís A. Alexandre,et al. On combining classifiers using sum and product rules , 2001, Pattern Recognit. Lett..
[65] Kiyohiro Shikano,et al. Julius - an open source real-time large vocabulary recognition engine , 2001, INTERSPEECH.
[66] E. Lehmann. Elements of large-sample theory , 1998 .
[67] Antonis A. Argyros,et al. Efficient model-based 3D tracking of hand articulations using Kinect , 2011, BMVC.
[68] Bo Chen,et al. Deep Learning of Invariant Spatio-Temporal Features from Video , 2010 .
[69] Wen Gao,et al. Large-Vocabulary Continuous Sign Language Recognition Based on Transition-Movement Models , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[70] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.