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We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN).
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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On space-time interest points
Ivan Laptev · 2005
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Unsupervised learning of human action categories using spatial-temporal words
Juan Carlos Niebles, Hongcheng Wang, and Li Fei-Fei · 2006
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Learning realistic human actions from movies
I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Recognizing realistic actions from videos “in the wild”
J. Liu, Jiebo Luo, and M. Shah · 2009
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Evaluation of local spatio-temporal features for action recognition
Heng Wang, Muhammad Muneeb Ullah, Alexander Klaser, Ivan Laptev, and Cordelia Schmid · 2009
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Modeling temporal structure of decomposable motion segments for activity classification
Juan Carlos Niebles, Chih-Wei Chen, and Li Fei-Fei · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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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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3D convolutional neural networks for human action recognition
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu · 2013
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Hmdb51: A large video database for human motion recognition
Hilde Kuehne, Hueihan Jhuang, Rainer Stiefelhagen, and Thomas Serre · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
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Two-Stream Convolutional Networks for Action Recognition in Videos
Karen Simonyan and Andrew Zisserman · 2014
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Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Beyond Short Snippets: Deep Networks for Video Classification
Joe Yue-Hei Ng, Matthew J. Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, and George Toderici · 2015
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Learning spatiotemporal features with 3D convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 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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Parallel separable 3d convolution for video and volumetric data understanding
Felix Gonda, Donglai Wei, Toufiq Parag, and Hanspeter Pfister · 2018
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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet
Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh · 2018
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What makes a video a video: Analyzing temporal information in video understanding models and datasets
De-An Huang, Vignesh Ramanathan, Dhruv Mahajan, Lorenzo Torresani, Manohar Paluri, Li Fei-Fei, and Juan Carlos Niebles · 2018
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Self-supervised video representation learning with space-time cubic puzzles
Dahun Kim, Donghyeon Cho, and In So Kweon · 2018
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Cooperative learning of audio and video models from self-supervised synchronization
Bruno Korbar, Du Tran, and Lorenzo Torresani · 2018
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Shuffle and Learn: Unsupervised Learning using Temporal Order Verification
Ishan Misra, C. Lawrence Zitnick, and Martial Hebert · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Temporal segment networks: towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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The” something something” video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al · 2017
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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
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Data distillation: Towards omni-supervised learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He · 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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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
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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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Large-scale weakly-supervised pre-training for video action recognition
Deepti Ghadiyaram, Du Tran, and Dhruv Mahajan · 2019
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Distinit: Learning video representations without a single labeled video
Rohit Girdhar, Du Tran, Lorenzo Torresani, and Deva Ramanan · 2019
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Video representation learning by dense predictive coding
Tengda Han, Weidi Xie, and Andrew Zisserman · 2019
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Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2019
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Only time can tell: Discovering temporal data for temporal modeling
Laura Sevilla-Lara, Shengxin Zha, Zhicheng Yan, Vedanuj Goswami, Matt Feiszli, and Lorenzo Torresani · 2019
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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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