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A recent work from Bello shows that training and scaling strategies may be more significant than model architectures for visual recognition.
Convolutional learning of spatio-temporal features
Graham W Taylor, Rob Fergus, Yann LeCun, and Christoph Bregler · 2010
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 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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Improved regularization of convolutional neural networks with cutout
Terrance Devries and Graham W. Taylor · 2017
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The kinetics human action video dataset
Will Kay, João Carreira, K. Simonyan, B. Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, T. Back, A. Natsev, Mustafa Suleyman, and Andrew Zisserman · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
A short note about kinetics-600, 2018
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 2018
Earlier work this paper cites.
Dropblock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V. Le · 2018
Earlier work this paper cites.
Squeeze-and-excitation networks
Jie Hu, L. Shen, and Gang Sun · 2018
Earlier work this paper cites.
A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, L. Torresani, Jamie Ray, Y. LeCun, and Manohar Paluri · 2018
Earlier work this paper cites.
Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Attention augmented convolutional networks
Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V. Le · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le · 2019
Cited alongside, same era.
Randaugment: Practical data augmentation with no separate search
E. D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2019
Cited alongside, same era.
Learning video representations from textual web supervision
Jonathan C Stroud, David A Ross, Chen Sun, Jia Deng, Rahul Sukthankar, and Cordelia Schmid · 2020
Later among the works it cites.
Random erasing data augmentation
Zhun Zhong, L. Zheng, Guoliang Kang, Shaozi Li, and Y. Yang · 2020
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Vivit: A video vision transformer
A. Arnab, M. Dehghani, G. Heigold, Chen Sun, Mario Lucic, and C. Schmid · 2021
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Lambdanetworks: Modeling long-range interactions without attention
Irwan Bello · 2021
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Revisiting resnets: Improved training and scaling strategies
Irwan Bello, William Fedus, Xianzhi Du, Ekin D Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, and Barret Zoph · 2021
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
Cited alongside, same era.
Bag of tricks for image classification with convolutional neural networks
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2019
Cited alongside, same era.
Stand-alone self-attention in vision models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jonathon Shlens · 2019
Cited alongside, same era.
Video classification with channel-separated convolutional networks
Du Tran, Heng Wang, L. Torresani, and Matt Feiszli · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Y. Yoo · 2019
Cited alongside, same era.
X3d: Expanding architectures for efficient video recognition
Christoph Feichtenhofer · 2020
Cited alongside, same era.
Big transfer (bit): General visual representation learning
Alexander Kolesnikov, L. Beyer, Xiaohua Zhai, J. Puigcerver, Jessica Yung, S. Gelly, and N. Houlsby · 2020
Cited alongside, same era.
Is space-time attention all you need for video understanding?
Gedas Bertasius, Heng Wang, and L. Torresani · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Simple training strategies and model scaling for object detection
Xianzhi Du, Barret Zoph, W. Hung, and Tsung-Yi Lin · 2021
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Multiscale vision transformers
Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, J. Malik, and Christoph Feichtenhofer · 2021
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Video swin transformer, 2021
Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu · 2021
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Daniel Neimark, O. Bar, Maya Zohar, and Dotan Asselmann · 2021
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Spatiotemporal contrastive video representation learning
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui · 2021
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