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Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy.
Long short-term memory
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End-to-end learning of action detection from frame glimpses in videos
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Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Spatiotemporal multiplier networks for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Richard P Wildes · 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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A unified personalized video recommendation via dynamic recurrent neural networks
Junyu Gao, Tianzhu Zhang, and Changsheng Xu · 2017
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The kinetics human action video dataset
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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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 · 2017
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Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q Weinberger · 2018
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Exploiting feature and class relationships in video categorization with regularized deep neural networks
Y.-G. Jiang, Z. Wu, J. Wang, X. Xue, and S.-F. Chang · 2018
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Tea: Temporal excitation and aggregation for action recognition
Yan Li, Bin Ji, Xintian Shi, Jianguo Zhang, Bin Kang, and Limin Wang · 2020
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Teinet: Towards an efficient architecture for video recognition
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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
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