Fetching the paper…
Reading the bibliography…
Recent progress in vision Transformers exhibits great success in various tasks driven by the new spatial modeling mechanism based on dot-product self-attention.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Interaction terms in logit and probit models
Chunrong Ai and Edward C Norton · 2003
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu 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
Earlier work this paper cites.
Dynamic filter networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 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.
SORT: second-order response transform for visual recognition
Yan Wang, Lingxi Xie, Chenxi Liu, Siyuan Qiao, Ya Zhang, Wenjun Zhang, Qi Tian, and Alan L. Yuille · 2017
Earlier work this paper cites.
Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
Earlier work this paper cites.
Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
Earlier work this paper cites.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Earlier work this paper cites.
Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
Earlier work this paper cites.
Hybrid task cascade for instance segmentation
Kai Chen, Jiangmiao Pang, Jiaqi Wang, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jianping Shi, Wanli Ouyang, et al · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Dynamic convolution: Attention over convolution kernels
Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu · 2020
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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, Jakob Uszkoreit, and Neil Houlsby · 2020
Cited alongside, same era.
Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Cited alongside, same era.
Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
Later among the works it cites.
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
Later among the works it cites.
Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
Later among the works it cites.
Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross Girshick · 2021
Later among the works it cites.
Focal attention for long-range interactions in vision transformers
Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, and Jianfeng Gao · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
Cited alongside, same era.
Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
Cited alongside, same era.
Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2021
Cited alongside, same era.
Per-pixel classification is not all you need for semantic segmentation
Bowen Cheng, Alex Schwing, and Alexander Kirillov · 2021
Cited alongside, same era.
Twins: Revisiting the design of spatial attention in vision transformers
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen · 2021
Cited alongside, same era.
Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc V Le, and Mingxing Tan · 2021
Cited alongside, same era.
Convit: Improving vision transformers with soft convolutional inductive biases
Stéphane d’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun · 2021
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Zihang Jiang, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
Later among the works it cites.
Vision transformer adapter for dense predictions
Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao · 2022
Closest in time.
Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
Closest in time.
Mixformer: End-to-end tracking with iterative mixed attention
Yutao Cui, Jiang Cheng, Limin Wang, and Gangshan Wu · 2022
Closest in time.
Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou, Jungong Han, Guiguang Ding, and Jian Sun · 2022
Closest in time.
Cswin transformer: A general vision transformer backbone with cross-shaped windows
Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, and Baining Guo · 2022
Closest in time.
Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, and Shi-Min Hu · 2022
Closest in time.
Exploring plain vision transformer backbones for object detection
Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He · 2022
Closest in time.
Mvitv2: Improved multiscale vision transformers for classification and detection
Yanghao Li, Chao-Yuan Wu, Haoqi Fan, Karttikeya Mangalam, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer · 2022
Closest in time.
Dynamic group transformer: A general vision transformer backbone with dynamic group attention
Kai Liu, Tianyi Wu, Cong Liu, and Guodong Guo · 2022
Closest in time.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Closest in time.
Maxim: Multi-axis mlp for image processing
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
Closest in time.
Maxvit: Multi-axis vision transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
Closest in time.
Pale transformer: A general vision transformer backbone with pale-shaped attention
Sitong Wu, Tianyi Wu, Haoru Tan, and Guodong Guo · 2022
Closest in time.
Multiview transformers for video recognition
Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, and Cordelia Schmid · 2022
Closest in time.
Jianwei Yang, Chunyuan Li, and Jianfeng Gao · 2022
Closest in time.
Dino: Detr with improved denoising anchor boxes for end-to-end object detection
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum · 2022
Closest in time.