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Recently, vision transformers started to show impressive results which outperform large convolution based models significantly.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
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Microsoft coco: Common objects in context
T. Y. Lin, M. Maire, S. Belongie, J. Hays, and C. L. Zitnick · 2014
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The pascal visual object classes challenge: A retrospective
M. Everingham, Sma Eslami, L Van Gool, Cki Williams, J. Winn, and A. Zisserman · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 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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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Cbam: Convolutional block attention module
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon · 2018
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Onnx: Open neural network exchange
Junjie Bai, Fang Lu, Ke Zhang, et al · 2019
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Tinynet: a lightweight, modular, and unified network architecture for the internet of things
Gonglong Chen, Yihui Wang, Huikang Li, and Wei Dong · 2019
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Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Ccnet: Criss-cross attention for semantic segmentation
Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
Levit: a vision transformer in convnet’s clothing for faster inference
Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze · 2021
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Cmt: Convolutional neural networks meet vision transformers
Jianyuan Guo, Kai Han, Han Wu, Chang Xu, Yehui Tang, Chunjing Xu, and Yunhe Wang · 2021
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Rethinking spatial dimensions of vision transformers
Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, and Seong Joon Oh · 2021
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Micronet: Improving image recognition with extremely low flops
Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Lei Zhang, and Nuno Vasconcelos · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
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, et al · 2020
Cited alongside, same era.
Ghostnet: More features from cheap operations
Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu · 2020
Cited alongside, same era.
Mnn: A universal and efficient inference engine
Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, et al · 2020
Cited alongside, same era.
Mobile-former: Bridging mobilenet and transformer
Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Xiaoyi Dong, Lu Yuan, and Zicheng Liu · 2021
Cited alongside, same era.
Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc Le, and Mingxing Tan · 2021
Cited alongside, same era.
Bottleneck transformers for visual recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, and Ashish Vaswani · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
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Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
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Early convolutions help transformers see better
Tete Xiao, Piotr Dollar, Mannat Singh, Eric Mintun, Trevor Darrell, and Ross Girshick · 2021
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Metaformer is actually what you need for vision
Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, and Shuicheng Yan · 2021
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Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Closest in time.
Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer
Sachin Mehta and Mohammad Rastegari · 2022
Closest in time.