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Shift equivariance is a fundamental principle that governs how we perceive the world - our recognition of an object remains invariant with respect to shifts.
Why do deep convolutional networks generalize so poorly to small image transformations?
Aharon Azulay and Yair Weiss · 2018
Earlier work this paper cites.
Making convolutional networks shift-invariant again, 2019
Richard Zhang · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Truly shift-invariant convolutional neural networks
Anadi Chaman and Ivan Dokmanic · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Coatnet: Marrying convolution and attention for all data sizes, 2021
Zihang Dai, Hanxiao Liu, Quoc V. Le, and Mingxing Tan · 2021
Earlier work this paper cites.
Cswin transformer: A general vision transformer backbone with cross-shaped windows, 2021
Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, and Baining Guo · 2021
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Multi-scale vision longformer: A new vision transformer for high-resolution image encoding
Pengchuan Zhang, Xiyang Dai, Jianwei Yang, Bin Xiao, Lu Yuan, Lei Zhang, and Jianfeng Gao · 2021
Cited alongside, same era.
Equivariant self-supervised learning: Encouraging equivariance in representations
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, and Marin Soljacic · 2022
Cited alongside, same era.
The lie derivative for measuring learned equivariance, 2022
Nate Gruver, Marc Finzi, Micah Goldblum, and Andrew Gordon Wilson · 2022
Later among the works it cites.
Swin transformer v2: Scaling up capacity and resolution, 2022
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, and Baining Guo · 2022
Later among the works it cites.
Maxvit: Multi-axis vision transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
Later among the works it cites.
What should be equivariant in self-supervised learning
Yuyang Xie, Jianhong Wen, Kin Wai Lau, Yasar Abbas Ur Rehman, and Jiajun Shen · 2022
Later among the works it cites.
Conditional positional encodings for vision transformers, 2023
Xiangxiang Chu, Zhi Tian, Bo Zhang, Xinlong Wang, and Chunhua Shen · 2023
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Davit: Dual attention vision transformers
Mingyu Ding, Bin Xiao, Noel Codella, Ping Luo, Jingdong Wang, and Lu Yuan · 2022
Cited alongside, same era.