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Current state-of-the-art models for video action recognition are mostly based on expensive 3D ConvNets.
Human activity analysis: A review
J.K. Aggarwal and M.S. Ryoo · 2011
Earlier work this paper cites.
HMDB: a large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, Mubarak Shah, Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
THUMOS challenge: Action recognition with a large number of classes
Y.-G. Jiang, J. Liu, A. Roshan Zamir, G. Toderici, I. Laptev, M. Shah, and R. Sukthankar · 2014
Earlier work this paper cites.
Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Activitynet: A large-scale video benchmark for human activity understanding
Bernard Ghanem Fabian Caba Heilbron, Victor Escorcia and Juan Carlos Niebles · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning Spatiotemporal Features With 3D Convolutional Networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Cited alongside, same era.
Learning spatio-temporal features with 3d residual networks for action recognition
Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh · 2017
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Temporal relational reasoning in videos
Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba · 2018
Cited alongside, same era.
Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2018
Later among the works it cites.
Videos as space-time region graphs
Xiaolong Wang and Abhinav Gupta · 2018
Later among the works it cites.
Eco: Efficient convolutional network for online video understanding
Mohammadreza Zolfaghari, Kamaljeet Singh, and Thomas Brox · 2018
Later among the works it cites.
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
Later among the works it cites.
Appearance-and-relation networks for video classification
Limin Wang, Wei Li, Wen Li, and Luc Van Gool · 2018
Later among the works it cites.
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Temporal shift module for efficient video understanding
Ji Lin, Chuang Gan, and Song Han · 2018
Cited alongside, same era.
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
Cited alongside, same era.
Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2018
Cited alongside, same era.
A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
Cited alongside, same era.
Big-little net: An efficient multi-scale feature representation for visual and speech recognition
Chun-Fu (Richard) Chen, Quanfu Fan, Neil Mallinar, Tom Sercu, and Rogerio Feris · 2019
Closest in time.
Moments in time dataset: one million videos for event understanding
Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Yan Yan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, et al · 2019
Closest in time.
Timeception for complex action recognition
Noureldien Hussein, Efstratios Gavves, and Arnold W.M. Smeulders · 2019
Closest in time.