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Dot-product attention has wide applications in computer vision and natural language processing.
THUMOS challenge: Action recognition with a large number of classes
Y.-G. Jiang, J. Liu, A. Roshan Zamir, G. Toderici, I. Laptev, M. Shah, and R. Sukthankar · 2014
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Tsung-Yi Lin, Piotr Dollár, Ross B Girshick, Kaiming He, Bharath Hariharan, and Serge J Belongie · 2017
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Jiahao Pang, Wenxiu Sun, Jimmy SJ Ren, Chengxi Yang, and Qiong Yan · 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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Songyang Zhang, Shipeng Yan, and Xuming He · 2017
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Pyramid stereo matching network
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Xinjing Cheng, Peng Wang, and Ruigang Yang · 2018
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Xingyu Liao, Lingxiao He, and Zhouwang Yang · 2018
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Compact generalized non-local network
Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, and Fuxin Xu · 2018
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Self-attention generative adversarial networks
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Very deep self-attention networks for end-to-end speech recognition
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Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, and Thomas S Huang · 2018
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Xiao Song, Xu Zhao, Hanwen Hu, and Liangji Fang · 2018
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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Cbam: Convolutional block attention module
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Xlnet: Generalized autoregressive pretraining for language understanding
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Hierarchical neural architecture search for deep stereo matching
Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Tom Drummond, Hongdong Li, and Zongyuan Ge · 2020
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