Structural-RNN: Deep Learning on Spatio-Temporal Graphs
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Silvio Savarese | Ashutosh Saxena | Amir Roshan Zamir | Ashesh Jain | S. Savarese | Ashutosh Saxena | A. Zamir | Ashesh Jain | Amir Zamir
[1] Yoshua Bengio,et al. Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models , 1993, NIPS.
[2] Christoph Goller,et al. Learning task-dependent distributed representations by backpropagation through structure , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[3] Yoshua Bengio,et al. Global training of document processing systems using graph transformer networks , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[4] Brendan J. Frey,et al. Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.
[5] X. Jin. Factor graphs and the Sum-Product Algorithm , 2002 .
[6] Ben Taskar,et al. Discriminative Probabilistic Models for Relational Data , 2002, UAI.
[7] Trevor Darrell,et al. Conditional Random Fields for Object Recognition , 2004, NIPS.
[8] Geoffrey E. Hinton,et al. Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.
[9] Matthew Richardson,et al. Markov logic networks , 2006, Machine Learning.
[10] Manuela M. Veloso,et al. Conditional random fields for activity recognition , 2007, AAMAS '07.
[11] Dieter Fox,et al. A Spatio-Temporal Probabilistic Model for Multi-Sensor Multi-Class Object Recognition , 2007, ISRR.
[12] Geoffrey E. Hinton,et al. The Recurrent Temporal Restricted Boltzmann Machine , 2008, NIPS.
[13] Cordelia Schmid,et al. Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[14] Ramakant Nevatia,et al. Key Object Driven Multi-category Object Recognition, Localization and Tracking Using Spatio-temporal Context , 2008, ECCV.
[15] David J. Fleet,et al. Topologically-constrained latent variable models , 2008, ICML '08.
[16] David J. Fleet,et al. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .
[17] David A. McAllester,et al. A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Geoffrey E. Hinton,et al. Factored conditional restricted Boltzmann Machines for modeling motion style , 2009, ICML '09.
[19] Larry S. Davis,et al. Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Andrew McCallum,et al. FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs , 2009, NIPS.
[21] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[22] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[23] Alexander G. Hauptmann,et al. MoSIFT : Recognizing Human Actions in Surveillance Videos CMU-CS-09-161 , 2009 .
[24] Thorsten Joachims,et al. Cutting-plane training of structural SVMs , 2009, Machine Learning.
[25] Alexander G. Hauptmann,et al. MoSIFT: Recognizing Human Actions in Surveillance Videos , 2009 .
[26] David J. Fleet,et al. Dynamical binary latent variable models for 3D human pose tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[27] Li Wang,et al. Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models , 2011, International Journal of Computer Vision.
[28] Ivan Laptev,et al. Track to the future: Spatio-temporal video segmentation with long-range motion cues , 2011, CVPR 2011.
[29] Andrew Y. Ng,et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.
[30] Vladlen Koltun,et al. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.
[31] William Brendel,et al. Learning spatiotemporal graphs of human activities , 2011, 2011 International Conference on Computer Vision.
[32] Andrew McCallum,et al. An Introduction to Conditional Random Fields , 2010, Found. Trends Mach. Learn..
[33] Ramakant Nevatia,et al. ACTIVE: Activity Concept Transitions in Video Event Classification , 2013, 2013 IEEE International Conference on Computer Vision.
[34] Alex Graves,et al. Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.
[35] Hema Swetha Koppula,et al. Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation , 2013, ICML.
[36] Cordelia Schmid,et al. Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.
[37] Hema Swetha Koppula,et al. Learning human activities and object affordances from RGB-D videos , 2012, Int. J. Robotics Res..
[38] Hedvig Kjellström,et al. Recognizing object affordances in terms of spatio-temporal object-object relationships , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.
[39] Navdeep Jaitly,et al. Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.
[40] Fei Wang,et al. Overtaking vehicle detection using a spatio-temporal CRF , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.
[41] Trevor Darrell,et al. PANDA: Pose Aligned Networks for Deep Attribute Modeling , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[42] Ken Perlin,et al. Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , 2014, ACM Trans. Graph..
[43] Cristian Sminchisescu,et al. Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[45] Yong Du,et al. Hierarchical recurrent neural network for skeleton based action recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Hema Swetha Koppula,et al. Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[47] Jitendra Malik,et al. Recurrent Network Models for Human Dynamics , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[48] Cees Snoek,et al. Objects2action: Classifying and Localizing Actions without Any Video Example , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[49] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[50] Fei-Fei Li,et al. Visualizing and Understanding Recurrent Networks , 2015, ArXiv.
[51] Cees G. M. Snoek,et al. Objects2action: Classifying and Localizing Actions without Any Video Example , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[52] Raquel Urtasun,et al. Fully Connected Deep Structured Networks , 2015, ArXiv.
[53] Xiaoxiao Li,et al. Semantic Image Segmentation via Deep Parsing Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[54] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[55] Nitish Srivastava,et al. Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.
[56] Marc'Aurelio Ranzato,et al. Learning Longer Memory in Recurrent Neural Networks , 2014, ICLR.
[57] Marcus Liwicki,et al. Scene labeling with LSTM recurrent neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Antoni B. Chan,et al. Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[59] Xinlei Chen,et al. Mind's eye: A recurrent visual representation for image caption generation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Alan L. Yuille,et al. Learning Deep Structured Models , 2014, ICML.
[61] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[62] Lorenzo Torresani,et al. Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[63] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] 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).
[65] Hema Swetha Koppula,et al. Anticipating Human Activities Using Object Affordances for Reactive Robotic Response , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[66] U. Austin,et al. Translating Videos to Natural Language Using Deep Recurrent Neural Networks , 2017 .