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Vision transformer (ViT) has recently shown its strong capability in achieving comparable results to convolutional neural networks (CNNs) on image classification.
Stand-alone self-attention in vision models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jonathon Shlens · 1906
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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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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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 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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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar · 2017
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 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
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. 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.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Local relation networks for image recognition
Han Hu, Zheng Zhang, Zhenda Xie, and Stephen Lin · 2019
Cited alongside, same era.
Panoptic feature pyramid networks
Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollar · 2019
Cited alongside, same era.
Is Space-Time Attention All You Need for Video Understanding?
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
Closest in time.
High-Performance Large-Scale Image Recognition Without Normalization
Andrew Brock, Soham De, Samuel L Smith, and Karen Simonyan · 2021
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ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Stéphane d’Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli, and Levent Sagun · 2021
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Ap-cnn: Weakly supervised attention pyramid convolutional neural network for fine-grained visual classification
Yifeng Ding, Zhanyu Ma, Shaoguo Wen, Jiyang Xie, Dongliang Chang, Zhongwei Si, Ming Wu, and Haibin Ling · 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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 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 Youngjoon Yoo · 2019
Cited alongside, same era.
Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo, and Tao Mei · 2019
Cited alongside, same era.
LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference
Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze · 2021
Closest in time.
Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, 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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Token Labeling: Training a 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet
Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Xiaojie Jin, Anran Wang, and Jiashi Feng · 2021
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LocalViT: Bringing Locality to Vision Transformers
Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool · 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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Bottleneck transformers for visual recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, and Ashish Vaswani · 2021
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Going deeper with Image Transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, 2021
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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An attention-driven hierarchical multi-scale representation for visual recognition, 2021
Zachary Wharton, Ardhendu Behera, and Asish Bera · 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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ConTNet: Why not use convolution and transformer at the same time?
Haotian Yan, Zhe Li, Weijian Li, Changhu Wang, Ming Wu, and Chuang Zhang · 2021
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Tokens-to-token vit: Training vision transformers from scratch on imagenet, 2021
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
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