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Spatiotemporal and motion features are two complementary and crucial information for video action recognition.
A duality based approach for realtime tv-l 1 optical flow
Christopher Zach, Thomas Pock, and Horst Bischof · 2007
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HMDB: a large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. 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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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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Action recognition with improved trajectories
Heng Wang and Cordelia Schmid · 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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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 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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Human action recognition using factorized spatio-temporal convolutional networks
Lin Sun, Kui Jia, Dit-Yan Yeung, and Bertram E Shi · 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.
Spatiotemporal residual networks for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Richard Wildes · 2016
Earlier work this paper cites.
Convolutional two-stream network fusion for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman · 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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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
Cited alongside, same era.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Cited alongside, same era.
Spatiotemporal multiplier networks for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Richard P Wildes · 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.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Cited alongside, same era.
Temporal shift module for efficient video understanding
Ji Lin, Chuang Gan, and Song Han · 2018
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D3d: Distilled 3d networks for video action recognition
Jonathan C Stroud, David A Ross, Chen Sun, Jia Deng, and Rahul Sukthankar · 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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Long-term temporal convolutions for action recognition
Gül Varol, Ivan Laptev, and Cordelia Schmid · 2018
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Learning spatio-temporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
Cited alongside, same era.
Optical flow estimation using a spatial pyramid network
Anurag Ranjan and Michael J Black · 2017
Cited alongside, same era.
Spatiotemporal pyramid network for video action recognition
Yunbo Wang, Mingsheng Long, Jianmin Wang, and Philip S Yu · 2017
Cited alongside, same era.
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
Cited alongside, same era.
End-to-end learning of motion representation for video understanding
Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, and Junzhou Huang · 2018
Cited alongside, same era.
Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2018
Cited alongside, same era.
Stnet: Local and global spatial-temporal modeling for action recognition
Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li, Xiao Liu, Yandong Li, Liming Wang, and Shilei Wen · 2018
Cited alongside, same era.
Appearance-and-relation networks for video classification
Limin Wang, Wei Li, Wen Li, and Luc Van Gool · 2018
Later among the works it cites.
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Videos as space-time region graphs
Xiaolong Wang and Abhinav Gupta · 2018
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Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2018
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Recognize actions by disentangling components of dynamics
Yue Zhao, Yuanjun Xiong, and Dahua Lin · 2018
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Temporal relational reasoning in videos
Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba · 2018
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Mict: Mixed 3d/2d convolutional tube for human action recognition
Yizhou Zhou, Xiaoyan Sun, Zheng-Jun Zha, and Wenjun Zeng · 2018
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Eco: Efficient convolutional network for online video understanding
Mohammadreza Zolfaghari, Kamaljeet Singh, and Thomas Brox · 2018
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