Improving Human Intention Prediction Using Data Augmentation
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[1] Francesca Cordella,et al. Learning by Demonstration for Planning Activities of Daily Living in Rehabilitation and Assistive Robotics , 2017, IEEE Robotics and Automation Letters.
[2] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[3] Andrew Zisserman,et al. Convolutional Two-Stream Network Fusion for Video Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Ming Yang,et al. 3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] David Wingate,et al. Estimating Human Intent for Physical Human-Robot Co-Manipulation , 2017, ArXiv.
[6] Lin Zhang,et al. A Preliminary Study on a Robot's Prediction of Human Intention , 2017, 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER).
[7] Luc Van Gool,et al. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition , 2016, ECCV.
[8] Mark D. McDonnell,et al. Understanding Data Augmentation for Classification: When to Warp? , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
[9] Lorenzo Torresani,et al. Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[10] Mubarak Shah,et al. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.
[11] Cordelia Schmid,et al. Expanded Parts Model for Human Attribute and Action Recognition in Still Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[12] Nikolaos Papanikolopoulos,et al. Robot Surveillance and Security , 2016, Springer Handbook of Robotics, 2nd Ed..
[13] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[14] Ivan Laptev,et al. Learning person-object interactions for action recognition in still images , 2011, NIPS.
[15] Thomas Linner,et al. Construction Robots: Elementary Technologies and Single-Task Construction Robots , 2016 .
[16] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[17] Brian Scassellati,et al. A thermal emotion classifier for improved human-robot interaction , 2016, 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).
[18] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[19] Tao Mei,et al. Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Matthew J. Hausknecht,et al. Beyond short snippets: Deep networks for video classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Russell H. Taylor,et al. Medical robotics in computer-integrated surgery , 2003, IEEE Trans. Robotics Autom..
[23] Russell H. Taylor,et al. Medical robotics in computer-integrated surgery , 2003, IEEE Trans. Robotics Autom..
[24] Alexander Verl,et al. Cooperation of human and machines in assembly lines , 2009 .
[25] Jonathan Tompson,et al. Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation , 2014, NIPS.
[26] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Bernhard Schölkopf,et al. Anticipatory action selection for human-robot table tennis , 2017, Artif. Intell..
[28] Thomas Serre,et al. HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.
[29] Hema Swetha Koppula,et al. Anticipatory Planning for Human-Robot Teams , 2014, ISER.
[30] Xue Li,et al. Action recognition in still images using a combination of human pose and context information , 2012, 2012 19th IEEE International Conference on Image Processing.