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Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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David G Lowe · 2004
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Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Visualizing high-dimensional data using t-sne
Laurens van der Maaten and Geoffrey E. Hinton · 2008
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2009
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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HMDB: a large video database for human motion recognition
Hilde Kuehne, Hueihan Jhuang, Estibaliz Garrote, Tomaso Poggio, and Thomas Serre · 2011
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Action Recognition by Dense Trajectories
Heng Wang, Alexander Kläser, Cordelia Schmid, and Liu Cheng-Lin · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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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THUMOS challenge: Action recognition with a large number of classes
Yu-Gang Jiang, Jingen Liu, Amir Roshan Zamir, Ivan Laptev, Massimo Piccardi, Mubarak Shah, and Rahul Sukthankar · 2013
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Action recognition with improved trajectories
Heng Wang and Cordelia Schmid · 2013
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Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions
Yang Yang, Imran Saleemi, and Mubarak Shah · 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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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Trecvid 2014–an overview of the goals, tasks, data, evaluation mechanisms and metrics
Paul Over, Jon Fiscus, Greg Sanders, David Joy, Martial Michel, George Awad, Alan Smeaton, Wessel Kraaij, and Georges Quénot · 2014
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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
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Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Subhashini Venugopalan Marcus Rohrbach, 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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ActionVLAD: Learning spatio-temporal aggregation for action classification
Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, and Bryan Russell · 2017
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Accurate, large minibatch sgd: training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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The kinetics human action video dataset
Will Kay, João Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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Learning spatio-temporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Human action recognition using factorized spatio-temporal convolutional networks
Lin Sun, Kui Jia, Dit-Yan Yeung, and Bertram E Shi · 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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Youtube-8m: A large-scale video classification benchmark
Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan · 2016
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Spatiotemporal residual networks for video action recognition
Christoph Feichtenhofer, Alex Pinz, and Richard Wildes · 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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Convnet architecture search for spatiotemporal feature learning
Du Tran, Jamie Ray, Zheng Shou, Shih-Fu Chang, and Manohar Paluri · 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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SLAC: A sparsely labeled dataset for action classification and localization
Hang Zhao, Zhicheng Yan, Heng Wang, Lorenzo Torresani, and Antonio Torralba · 2017
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Places: A 10 million Image Database for Scene Recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Spatio-temporal channel correlation networks for action classification
Ali Diba, Mohsen Fayyaz, Vivek Sharma, M Mahdi Arzani, Rahman Yousefzadeh, Juergen Gall, and Luc Van Gool · 2018
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From lifestyle VLOGs to everyday interactions
David F. Fouhey, Weicheng Kuo, Alexei A. Efros, and Jitendra Malik · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Rohit Girdhar, João Carreira, Carl Doersch, and Andrew Zisserman · 2018
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Boosting self-supervised learning via knowledge transfer
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro, and Hamed Pirsiavash · 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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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, and Sergey Levine · 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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Taskonomy: Disentangling task transfer learning
Amir R. Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Video Action Transformer Network
Rohit Girdhar, João Carreira, Carl Doersch, and Andrew Zisserman · 2019
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