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Fine-grained action recognition is a challenging task in computer vision.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
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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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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
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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 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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Temporal segment networks for action recognition in videos
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2018
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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Temporal relational reasoning in videos
Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba · 2018
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Attention augmented convolutional networks
Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V Le · 2019
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
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Tsm: Temporal shift module for efficient video understanding
Ji Lin, Chuang Gan, and Song Han · 2019
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Mmaction, 2019
Yue Zhao, Yuanjun Xiong, and Dahua Lin · 2019
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Semi-cnn architecture for effective spatio-temporal learning in action recognition
Mei Chee Leong, Dilip K Prasad, Yong Tsui Lee, and Feng Lin · 2020
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Univl: A unified video and language pre-training model for multimodal understanding and generation
Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou · 2020
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Actbert: Learning global-local video-text representations
Linchao Zhu and Yi Yang · 2020
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Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
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Is space-time attention all you need for video understanding
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
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Bo Dai Dian Shao, Yue Zhao and Dahua Lin · 2020
Cited alongside, same era.
X3d: Expanding architectures for efficient video recognition
Christoph Feichtenhofer · 2020
Cited alongside, same era.
Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc Le, and Mingxing Tan · 2021
Later among the works it cites.
Joint learning on the hierarchy representation for fine-grained human action recognition
Mei Chee Leong, Hui Li Tan, Haosong Zhang, Liyuan Li, Feng Lin, and Joo Hwee Lim · 2021
Later among the works it cites.
Temporal query networks for fine-grained video understanding
Chuhan Zhang, Ankush Gupta, and Andrew Zisserman · 2021
Later among the works it cites.