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How Do Vision Transformers Work?
v1v2v3v4 (latest)

How Do Vision Transformers Work?

International Conference on Learning Representations (ICLR), 2022
14 February 2022
Namuk Park
Songkuk Kim
    ViT
ArXiv (abs)PDFHTMLGithub (815★)

Papers citing "How Do Vision Transformers Work?"

8 / 258 papers shown
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image
  Synthesis and Image Recognition
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognition
Jianghao Shen
Tianfu Wu
ViT
220
0
0
29 Dec 2021
MetaFormer Is Actually What You Need for Vision
MetaFormer Is Actually What You Need for VisionComputer Vision and Pattern Recognition (CVPR), 2021
Weihao Yu
Mi Luo
Pan Zhou
Chenyang Si
Yichen Zhou
Xinchao Wang
Jiashi Feng
Shuicheng Yan
507
1,192
0
22 Nov 2021
TransMorph: Transformer for unsupervised medical image registration
TransMorph: Transformer for unsupervised medical image registration
Junyu Chen
Eric C. Frey
Yufan He
W. Paul Segars
Ye Li
Yong Du
ViTMedIm
695
479
0
19 Nov 2021
A Survey of Visual Transformers
A Survey of Visual TransformersIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Yang Liu
Yao Zhang
Yixin Wang
Feng Hou
Jin Yuan
Jiang Tian
Yang Zhang
Peng Wang
Jianping Fan
Zhiqiang He
3DGSViT
467
477
0
11 Nov 2021
GeoT: A Geometry-aware Transformer for Reliable Molecular Property
  Prediction and Chemically Interpretable Representation Learning
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation LearningACS Omega (ACS Omega), 2021
Bumju Kwak
J. S. Park
Taewon Kang
Jeonghee Jo
Byunghan Lee
Sungroh Yoon
AI4CE
168
8
0
29 Jun 2021
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy,
  Uncertainty, and Robustness
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and RobustnessInternational Conference on Machine Learning (ICML), 2021
Namuk Park
S. Kim
UQCVAAML
290
24
0
26 May 2021
Local Convolutions Cause an Implicit Bias towards High Frequency
  Adversarial Examples
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
J. O. Caro
Yilong Ju
Ryan Pyle
Sourav Dey
Wieland Brendel
Fabio Anselmi
Ankit B. Patel
AAML
339
14
0
19 Jun 2020
On the Relationship between Self-Attention and Convolutional Layers
On the Relationship between Self-Attention and Convolutional LayersInternational Conference on Learning Representations (ICLR), 2019
Jean-Baptiste Cordonnier
Andreas Loukas
Martin Jaggi
565
607
0
08 Nov 2019
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