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In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time.
Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Ce Liu et al · 2009
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A new learning paradigm: Learning using privileged information
Vladimir Vapnik and Akshay Vashist · 2009
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Multiple choice learning: Learning to produce multiple structured outputs
Abner Guzman-Rivera, Dhruv Batra, and Pushmeet Kohli · 2012
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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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Cross-view action modeling, learning and recognition
Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu, and Song-Chun Zhu · 2014
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Long-term recurrent convolutional networks for visual recognition and description
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell · 2015
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Finding action tubes
Georgia Gkioxari and Jitendra Malik · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Unifying distillation and privileged information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, and Vladimir Vapnik · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Cross modal distillation for supervision transfer
Saurabh Gupta, Judy Hoffman, and Jitendra Malik · 2016
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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 with side information through modality hallucination
Judy Hoffman, Saurabh Gupta, and Trevor Darrell · 2016
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Stochastic multiple choice learning for training diverse deep ensembles
Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, and Dhruv Batra · 2016
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Spatio-temporal lstm with trust gates for 3d human action recognition
Jun Liu, Amir Shahroudy, Dong Xu, and Gang Wang · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Histogram of oriented principal components for cross-view action recognition
Hossein Rahmani, Arif Mahmood, Du Huynh, and Ajmal Mian · 2016
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Ntu rgb+ d: A large scale dataset for 3d human activity analysis
Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
Ensemble learning: A survey
Omer Sagi and Lior Rokach · 2018
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Optical flow guided feature: A fast and robust motion representation for video action recognition
Shuyang Sun, Zhanghui Kuang, Lu Sheng, Wanli Ouyang, and Wei Zhang · 2018
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A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
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Rgb-d-based human motion recognition with deep learning: A survey
Pichao Wang, Wanqing Li, Philip Ogunbona, Jun Wan, and Sergio Escalera · 2018
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Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 2018
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Mars: Motion-augmented rgb stream for action recognition
Nieves Crasto, Philippe Weinzaepfel, Karteek Alahari, and Cordelia Schmid · 2019
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Joao Carreira and Andrew Zisserman · 2017
Cited alongside, same era.
Going deeper into action recognition: A survey
Samitha Herath, Mehrtash Harandi, and Fatih Porikli · 2017
Cited alongside, same era.
Confident multiple choice learning
Kimin Lee, Changho Hwang, Kyoung Soo Park, and Jinwoo Shin · 2017
Cited alongside, same era.
Tommaso Furlanello, Zachary C Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar · 2018
Cited alongside, same era.
Modality distillation with multiple stream networks for action recognition
Nuno C. Garcia, Pietro Morerio, and Vittorio Murino · 2018
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Human action recognition and prediction: A survey
Yu Kong and Yun Fu · 2018
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Motion feature network: Fixed motion filter for action recognition
Myunggi Lee, Seungeui Lee, Sungjoon Son, Gyutae Park, and Nojun Kwak · 2018
Cited alongside, same era.
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Learning with privileged information via adversarial discriminative modality distillation
Nuno C Garcia, Pietro Morerio, and Vittorio Murino · 2019
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Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding
Jun Liu, Amir Shahroudy, Mauricio Lisboa Perez, Gang Wang, Ling-Yu Duan, and Alex Kot Chichung · 2019
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Representation flow for action recognition
AJ Piergiovanni and Michael S Ryoo · 2019
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Versatile multiple choice learning and its application to vision computing
Kai Tian, Yi Xu, Shuigeng Zhou, and Jihong Guan · 2019
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Unifying heterogeneous classifiers with distillation
Jayakorn Vongkulbhisal, Phongtharin Vinayavekhin, and Marco Visentini-Scarzanella · 2019
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Action recognition for depth video using multi-view dynamic images
Yang Xiao, Jun Chen, Yancheng Wang, Zhiguo Cao, Joey Tianyi Zhou, and Xiang Bai · 2019
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Snapshot distillation: Teacher-student optimization in one generation
Chenglin Yang, Lingxi Xie, Chi Su, and Alan L Yuille · 2019
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Dance with flow: Two-in-one stream action detection
Jiaojiao Zhao and Cees GM Snoek · 2019
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