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Temporal modelling is the key for efficient video action recognition.
Dynamic convolution: Attention over convolution kernels
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3d convolutional neural networks for human action recognition
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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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Conditional computation in neural networks for faster models
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Long-term recurrent convolutional networks for visual recognition and description
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Adaptive computation time for recurrent neural networks
Alex Graves · 2016
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Deep residual learning for image recognition
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Categorical reparameterization with gumbel-softmax
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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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End-to-end learning of action detection from frame glimpses in videos
Serena Yeung, Olga Russakovsky, Greg Mori, and Li Fei-Fei · 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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Spatially adaptive computation time for residual networks
Michael Figurnov, Maxwell D Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, and Ruslan Salakhutdinov · 2017
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The" something something" video database for learning and evaluating visual common sense
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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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Runtime neural pruning
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Mason McGill and Pietro Perona · 2017
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Spottune: transfer learning through adaptive fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, and Rogerio Feris · 2019
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Ghostnet: More features from cheap operations
Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu · 2019
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Channel gating neural networks
Weizhe Hua, Yuan Zhou, Christopher M De Sa, Zhiru Zhang, and G Edward Suh · 2019
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Stm: Spatiotemporal and motion encoding for action recognition
Boyuan Jiang, MengMeng Wang, Weihao Gan, Wei Wu, and Junjie Yan · 2019
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Scsampler: Sampling salient clips from video for efficient action recognition
Bruno Korbar, Du Tran, and Lorenzo Torresani · 2019
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Tsm: Temporal shift module for efficient video understanding
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Watching a small portion could be as good as watching all: Towards efficient video classification
Hehe Fan, Zhongwen Xu, Linchao Zhu, Chenggang Yan, Jianjun Ge, and Yi Yang · 2018
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2018
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Dynamic channel pruning: Feature boosting and suppression
Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, and Cheng-zhong Xu · 2018
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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
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Moments in time dataset: one million videos for event understanding
Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfruend, Carl Vondrick, et al · 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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Ji Lin, Chuang Gan, and Song Han · 2019
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The jester dataset: A large-scale video dataset of human gestures
Joanna Materzynska, Guillaume Berger, Ingo Bax, and Roland Memisevic · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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Video classification with channel-separated convolutional networks
Du Tran, Heng Wang, Lorenzo Torresani, and Matt Feiszli · 2019
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Dynamic convolutions: Exploiting spatial sparsity for faster inference
Thomas Verelst and Tinne Tuytelaars · 2019
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Condconv: Conditionally parameterized convolutions for efficient inference
Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam · 2019
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Dynamic motion representation for human action recognition
Sadjad Asghari-Esfeden, Mario Sznaier, and Octavia Camps · 2020
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Listen to look: Action recognition by previewing audio
Ruohan Gao, Tae-Hyun Oh, Kristen Grauman, and Lorenzo Torresani · 2020
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Learning 3d-aware egocentric spatial-temporal interaction via graph convolutional networks
Chengxi Li, Yue Meng, Stanley H Chan, and Yi-Ting Chen · 2020
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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
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Ar-net: Adaptive frame resolution for efficient action recognition
Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, and Rogerio Feris · 2020
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Gate-shift networks for video action recognition
Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz · 2020
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Dynamic inference: A new approach toward efficient video action recognition
Wenhao Wu, Dongliang He, Xiao Tan, Shifeng Chen, Yi Yang, and Shilei Wen · 2020
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Va-red 2 : Video adaptive redundancy reduction
Bowen Pan, Rameswar Panda, Camilo Luciano Fosco, Chung-Ching Lin, Alex J Andonian, Yue Meng, Kate Saenko, Aude Oliva, and Rogerio Feris · 2021
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