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With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Computer vision for sports: Current applications and research topics
Graham Thomas, Rikke Gade, Thomas B. Moeslund, Peter Carr, and Adrian Hilton · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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The kinetics human action video dataset
Andrew Zisserman, Joao Carreira, Karen Simonyan, Will Kay, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, and Mustafa Suleyman · 2017
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Soccernet: A scalable dataset for action spotting in soccer videos
Silvio Giancola, Mohieddine Amine, Tarek Dghaily, and Bernard Ghanem · 2018
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
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Large-scale weakly-supervised pre-training for video action recognition
Deepti Ghadiyaram, Matt Feiszli, Du Tran, Xueting Yan, Heng Wang, and D. Mahajan · 2019
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Video classification with channel-separated convolutional networks
Du Tran, Heng Wang, Matt Feiszli, and Lorenzo Torresani · 2019
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Using player’s body-orientation to model pass feasibility in soccer
A. Arbués-Sangüesa, A. Martín, J. Fernández, C. Ballester, and G. Haro · 2020
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Multimodal and multiview distillation for real-time player detection on a football field
Anthony Cioppa, Adrien Deliege, Noor Ul Huda, Rikke Gade, Marc Van Droogenbroeck, and Thomas B. Moeslund · 2020
Cited alongside, same era.
A context-aware loss function for action spotting in soccer videos
Omni-sourced webly-supervised learning for video recognition
Haodong Duan, Yue Zhao, Yuanjun Xiong, Wentao Liu, and Dahua Lin · 2020
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Gta: Global temporal attention for video action understanding
Bo He, Xitong Yang, Zuxuan Wu, Hao Chen, Ser-Nam Lim, and Abhinav Shrivastava · 2020
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Group activity detection from trajectory and video data in soccer
Ryan Sanford, Siavash Gorji, Luiz G. Hafemann, Bahareh Pourbabaee, and Mehrsan Javan · 2020
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Improved soccer action spotting using both audio and video streams
Bastien Vanderplaetse and Stéphane Dupont · 2020
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Temporal pyramid network for action recognition
Ceyuan Yang, Yinghao Xu, Jianping Shi, Bo Dai, and B. Zhou · 2020
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Anthony Cioppa, Adrien Deliège, Silvio Giancola, Bernard Ghanem, Marc Van Droogenbroeck, Rikke Gade, and Thomas B. Moeslund · 2020
Cited alongside, same era.
Soccernet-v2 : A dataset and benchmarks for holistic understanding of broadcast soccer videos
A. Deliège, A. Cioppa, Silvio Giancola, M. J. Seikavandi, J. V. Dueholm, Kamal Nasrollahi, Bernard Ghanem, T. Moeslund, and Marc Van Droogenbroeck · 2020
Cited alongside, same era.
Temporally-aware feature pooling for action spotting in video broadcasts
Silvio Giancola and Bernard Ghanem · 2021
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Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann · 2021
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