Early Action Prediction by Soft Regression
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Gang Wang | Jian-Huang Lai | Jianguo Zhang | Wei-Shi Zheng | Jian-Fang Hu | Lianyang Ma | G. Wang | J. Lai | Jianfang Hu | Weishi Zheng | Jianguo Zhang | Lianyang Ma
[1] Luc Van Gool,et al. Two-Stream SR-CNNs for Action Recognition in Videos , 2016, BMVC.
[2] Xiaodong Yang,et al. EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[3] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2015, CVPR.
[4] Shaogang Gong,et al. Recognising action as clouds of space-time interest points , 2009, CVPR.
[5] Jing Liu,et al. Robust Structured Subspace Learning for Data Representation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Larry S. Davis,et al. Objects in Action: An Approach for Combining Action Understanding and Object Perception , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Kilian Q. Weinberger,et al. Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.
[8] 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.
[9] Yong Du,et al. Hierarchical recurrent neural network for skeleton based action recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Gang Wang,et al. Real-Time RGB-D Activity Prediction by Soft Regression , 2016, ECCV.
[11] Gang Wang,et al. NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Hui Cheng,et al. Knowledge-guided recurrent neural network learning for task-oriented action prediction , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).
[13] Jian-Huang Lai,et al. Jointly Learning Heterogeneous Features for RGB-D Activity Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Jake K. Aggarwal,et al. Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[15] Junsong Yuan,et al. Learning Actionlet Ensemble for 3D Human Action Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Yun Fu,et al. Deep Sequential Context Networks for Action Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Marwan Torki,et al. Human Action Recognition Using a Temporal Hierarchy of Covariance Descriptors on 3D Joint Locations , 2013, IJCAI.
[18] Cewu Lu,et al. Range-Sample Depth Feature for Action Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[20] Yoshua Bengio,et al. Gated Feedback Recurrent Neural Networks , 2015, ICML.
[21] Gang Wang,et al. DAG-Recurrent Neural Networks for Scene Labeling , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Juan Carlos Niebles,et al. Discriminative Hierarchical Modeling of Spatio-temporally Composable Human Activities , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Gang Yu,et al. Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction , 2014, ACCV.
[24] P J Webros. BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .
[25] Ronan Collobert,et al. Recurrent Convolutional Neural Networks for Scene Labeling , 2014, ICML.
[26] Jake K. Aggarwal,et al. View invariant human action recognition using histograms of 3D joints , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[27] Cordelia Schmid,et al. Long-Term Temporal Convolutions for Action Recognition , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Gang Wang,et al. Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition , 2016, ECCV.
[29] Cordelia Schmid,et al. A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.
[30] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[31] Michael S. Ryoo,et al. Human activity prediction: Early recognition of ongoing activities from streaming videos , 2011, 2011 International Conference on Computer Vision.
[32] Gang Wang,et al. Global Context-Aware Attention LSTM Networks for 3D Action Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Stan Sclaroff,et al. Learning Activity Progression in LSTMs for Activity Detection and Early Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Xiaodong Yang,et al. Action Recognition Using Super Sparse Coding Vector with Spatio-temporal Awareness , 2014, ECCV.
[35] Jun Miao,et al. Activity Auto-Completion: Predicting Human Activities from Partial Videos , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[36] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[37] Ying Wu,et al. Robust 3D Action Recognition with Random Occupancy Patterns , 2012, ECCV.
[38] Yi Wang,et al. Sequential Max-Margin Event Detectors , 2014, ECCV.
[39] Hema Swetha Koppula,et al. Anticipating Human Activities Using Object Affordances for Reactive Robotic Response , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Cordelia Schmid,et al. Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.
[41] Fernando De la Torre,et al. Max-Margin Early Event Detectors , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[42] Gwenn Englebienne,et al. Learning to Recognize Human Activities from Soft Labeled Data , 2014, Robotics: Science and Systems.
[43] Luc Van Gool,et al. UntrimmedNets for Weakly Supervised Action Recognition and Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] 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.
[45] Ruzena Bajcsy,et al. Sequence of the Most Informative Joints (SMIJ): A new representation for human skeletal action recognition , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[46] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[47] Yun Fu,et al. A Discriminative Model with Multiple Temporal Scales for Action Prediction , 2014, ECCV.
[48] Andrew W. Fitzgibbon,et al. Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.
[49] Gang Wang,et al. Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[50] Nanning Zheng,et al. Modeling 4D Human-Object Interactions for Event and Object Recognition , 2013, 2013 IEEE International Conference on Computer Vision.
[51] Martial Hebert,et al. Activity Forecasting , 2012, ECCV.
[52] Jian-Huang Lai,et al. Exemplar-Based Recognition of Human–Object Interactions , 2016, IEEE Transactions on Circuits and Systems for Video Technology.
[53] Yun Fu,et al. Max-Margin Action Prediction Machine , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[54] Antonio Torralba,et al. Anticipating the future by watching unlabeled video , 2015, ArXiv.
[55] Tinne Tuytelaars,et al. Rank Pooling for Action Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[56] Hema Swetha Koppula,et al. Learning human activities and object affordances from RGB-D videos , 2012, Int. J. Robotics Res..
[57] Xiaodong Yang,et al. Super Normal Vector for Activity Recognition Using Depth Sequences , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[58] Mubarak Shah,et al. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.
[59] Tian-Tsong Ng,et al. Multimodal Multipart Learning for Action Recognition in Depth Videos , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[60] Zicheng Liu,et al. HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[61] Yun Fu,et al. Prediction of Human Activity by Discovering Temporal Sequence Patterns , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[62] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[63] Sven J. Dickinson,et al. Recognize Human Activities from Partially Observed Videos , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[64] Limin Wang,et al. Action recognition with trajectory-pooled deep-convolutional descriptors , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[65] Shuicheng Yan,et al. Semantic Object Parsing with Graph LSTM , 2016, ECCV.
[66] Bin Sun,et al. Action Prediction From Videos via Memorizing Hard-to-Predict Samples , 2018, AAAI.
[67] Shih-Fu Chang,et al. CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[68] Silvio Savarese,et al. A Hierarchical Representation for Future Action Prediction , 2014, ECCV.