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We introduce a new high resolution, high frame rate stereo video dataset, which we call SPIN, for tracking and action recognition in the game of ping pong.
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Long short-term memory
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Mark L Nagurka · 2003
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Dynamic model based ball trajectory prediction for a robot ping-pong player
Xiaopeng Chen, Ye Tian, Qiang Huang, Weimin Zhang, and Zhangguo Yu · 2010
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Towards understanding action recognition
H. Jhuang, J. Gall, S. Zuffi, C. Schmid, and M. J. Black · 2013
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The motion of an arbitrarily rotating spherical projectile and its application to ball games
Garry Robinson and Ian Robinson · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Multiple object tracking: A literature review
Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Xiaowei Zhao, and Tae-Kyun Kim · 2014
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Activitynet: A large-scale video benchmark for human activity understanding
Bernard Ghanem Fabian Caba Heilbron, Victor Escorcia and Juan Carlos Niebles · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Sports videos in the wild (svw): A video dataset for sports analysis
Seyed Morteza Safdarnejad, Xiaoming Liu, Lalita Udpa, Brooks Andrus, John Wood, and Dean Craven · 2015
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Action recognition using visual attention
Shikhar Sharma, Ryan Kiros, and Ruslan Salakhutdinov · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang Dit-Yan Yeung, Wai kin Wong, and Wang chun Woo · 2015
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Learning to track: Online multi-object tracking by decision making
Yu Xiang, Alexandre Alahi, and Silvio Savarese · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning to track at 100 fps with deep regression networks
David Held, Sebastian Thrun, and Silvio Savarese · 2016
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Detecting events and key actors in multi-person videos
Optical flow guided feature: A fast and robust motion representation for video action recognition
Shuyang Sun, Zhanghui Kuang, Wanli Ouyang, Lu Sheng, and Wei Zhang · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Non-local neural networks
Xiaolong Wang, Ross B. Girshick, Abhinav Gupta, and Kaiming He · 2017
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Rethinking spatiotemporal feature learning for video understanding
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2017
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A2-nets: Double attention networks
Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, and Jiashi Feng · 2018
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Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, and Li Fei-Fei · 2016
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Hollywood in homes: Crowdsourcing data collection for activity understanding
Gunnar A. Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta · 2016
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Wavenet: A generative model for raw audio
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, and Koray Kavukcuoglu · 2016
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The physics of juggling a spinning ping-pong ball
Ralf Widenhorn · 2016
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2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Quo vadis, action recognition? a new model and the kinetics dataset · 2017
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Detect to track and track to detect
Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman · 2017
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Attentional pooling for action recognition
Rohit Girdhar and Deva Ramanan · 2017
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Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2018
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Tracking noisy targets: A review of recent object tracking approaches
Mustansar Fiaz, Arif Mahmood, and Soon Ki Jung · 2018
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Video action transformer network
Rohit Girdhar, João Carreira, Carl Doersch, and Andrew Zisserman · 2018
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A proposal-based solution to spatio-temporal action detection in untrimmed videos
Joshua Gleason, Rajeev Ranjan, Steven Schwarcz, Carlos Castillo, Jun-Cheng Chen, and Rama Chellappa · 2018
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Moments in time dataset: one million videos for event understanding
Mathew Monfort, Bolei Zhou, Sarah Adel Bargal, Alex Andonian, Tom Yan, Kandan Ramakrishnan, Lisa M. Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, and Aude Oliva · 2018
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Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model
George Papandreou, Tyler Zhu, Liang-Chieh Chen, Spyros Gidaris, Jonathan Tompson, and Kevin Murphy · 2018
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Representation flow for action recognition
A. J. Piergiovanni and Michael S. Ryoo · 2018
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A closer look at spatiotemporal convolutions for action recognition
D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri · 2018
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Dynamonet: Dynamic action and motion network
Ali Diba, Vivek Sharma, Luc Van Gool, and Rainer Stiefelhagen · 2019
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Stochastic prediction of multi-agent interactions from partial observations
Chen Sun, Per Karlsson, Jiajun Wu, Joshua B Tenenbaum, and Kevin Murphy · 2019
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