Fetching the paper…
Reading the bibliography…
Temporal modeling still remains challenging for action recognition in videos.
Determining optical flow
Berthold K. P. Horn and Brian G. Schunck · 1981
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
3d convolutional neural networks for human action recognition
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu · 2010
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Fei-Fei Li · 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.
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.
Beyond short snippets: Deep networks for video classification
Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, and George Toderici · 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.
Action recognition with trajectory-pooled deep-convolutional descriptors
Limin Wang, Yu Qiao, and Xiaoou Tang · 2015
Earlier work this paper cites.
Convolutional two-stream network fusion for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Earlier work this paper cites.
Quo vadis, action recognition? A new model and the kinetics dataset
João Carreira and Andrew Zisserman · 2017
Earlier work this paper cites.
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 Fründ, Peter Yianilos, Moritz Mueller-Freitag, Florian Hoppe, Christian Thurau, Ingo Bax, and Roland Memisevic · 2017
Earlier work this paper cites.
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, Mustafa Suleyman, and Andrew Zisserman · 2017
Earlier work this paper cites.
Learning spatio-temporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
Cited alongside, same era.
Temporal difference networks for video action recognition
Joe Yue-Hei Ng and Larry S Davis · 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.
Long-term temporal convolutions for action recognition
Gül Varol, Ivan Laptev, and Cordelia Schmid · 2018
Cited alongside, same era.
Appearance-and-relation networks for video classification
Limin Wang, Wei Li, Wen Li, and Luc Van Gool · 2018
Cited alongside, same era.
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
Cited alongside, same era.
TSM: temporal shift module for efficient video understanding
Ji Lin, Chuang Gan, and Song Han · 2019
Later among the works it cites.
Grouped spatial-temporal aggregation for efficient action recognition
Chenxu Luo and Alan L Yuille · 2019
Later among the works it cites.
Learning spatio-temporal representation with local and global diffusion
Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Xinmei Tian, and Tao Mei · 2019
Later among the works it cites.
Video classification with channel-separated convolutional networks
Du Tran, Heng Wang, Matt Feiszli, and Lorenzo Torresani · 2019
Later among the works it cites.
X3d: Expanding architectures for efficient video recognition
Christoph Feichtenhofer · 2020
Closest in time.
Smallbignet: Integrating core and contextual views for video classification
Xianhang Li, Yali Wang, Zhipeng Zhou, and Yu Qiao · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Videos as space-time region graphs
Xiaolong Wang and Abhinav Gupta · 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.
Recognize actions by disentangling components of dynamics
Yue Zhao, Yuanjun Xiong, and Dahua Lin · 2018
Cited alongside, same era.
Temporal relational reasoning in videos
Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba · 2018
Cited alongside, same era.
ECO: efficient convolutional network for online video understanding
Mohammadreza Zolfaghari, Kamaljeet Singh, and Thomas Brox · 2018
Cited alongside, same era.
More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation
Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, and David Cox · 2019
Cited alongside, same era.
Closest in time.
Tea: Temporal excitation and aggregation for action recognition
Yan Li, Bin Ji, Xintian Shi, Jianguo Zhang, Bin Kang, and Limin Wang · 2020
Closest in time.
Teinet: Towards an efficient architecture for video recognition
Zhaoyang Liu, Donghao Luo, Yabiao Wang, Limin Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Tong Lu · 2020
Closest in time.
TAM: temporal adaptive module for video recognition
Zhaoyang Liu, Limin Wang, Wayne Wu, Chen Qian, and Tong Lu · 2020
Closest in time.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2020
Closest in time.
Temporal interlacing network
Hao Shao, Shengju Qian, and Yu Liu · 2020
Closest in time.
Gate-shift networks for video action recognition
Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz · 2020
Closest in time.
Video modeling with correlation networks
Heng Wang, Du Tran, Lorenzo Torresani, and Matt Feiszli · 2020
Closest in time.
V4d: 4d convolutional neural networks for video-level representation learning
Shiwen Zhang, Sheng Guo, Weilin Huang, Matthew R Scott, and Limin Wang · 2020
Closest in time.
CT-net: Channel tensorization network for video classification
Kunchang Li, Xianhang Li, Yali Wang, Jun Wang, and Yu Qiao · 2021
Closest in time.