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For computer vision, Vision Transformers (ViTs) have become one of the go-to deep net architectures.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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M. Lin, Q. Chen, and S. Yan · 2013
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst · 2013
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Foundations of signal processing
M. Vetterli, J. Kovačević, and V. K. Goyal · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Group equivariant convolutional networks
T. Cohen and M. Welling · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola · 2017
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Scene parsing through ADE20K dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
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Spherical CNNs
T. S. Cohen, M. Geiger, J. Köhler, and M. Welling · 2018
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Deep models of interactions across sets
J. Hartford, D. Graham, K. Leyton-Brown, and S. Ravanbakhsh · 2018
Cited alongside, same era.
Clebsch–Gordan Nets: a fully Fourier space spherical convolutional neural network
R. Kondor, Z. Lin, and S. Trivedi · 2018
Cited alongside, same era.
MobileNetV2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
Cited alongside, same era.
Unified perceptual parsing for scene understanding
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun · 2018
Cited alongside, same era.
Why do deep convolutional networks generalize so poorly to small image transformations?
A. Azulay and Y. Weiss · 2019
Cited alongside, same era.
Gauge equivariant convolutional networks and the icosahedral CNN
T. Cohen, M. Weiler, B. Kicanaoglu, and M. Welling · 2019
Cited alongside, same era.
Gauge equivariant mesh CNNs: Anisotropic convolutions on geometric graphs
P. de Haan, M. Weiler, T. Cohen, and M. Welling · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2021
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Multiscale vision transformers
H. Fan, B. Xiong, K. Mangalam, Y. Li, Z. Yan, J. Malik, and C. Feichtenhofer · 2021
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Equivariant networks for pixelized spheres
M. Shakerinava and S. Ravanbakhsh · 2021
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CvT: Introducing convolutions to vision transformers
H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang · 2021
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A survey on vision transformer
K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, et al · 2022
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Invariant and equivariant graph networks
H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman · 2019
Cited alongside, same era.
General E(2)-equivariant steerable CNNs
M. Weiler and G. Cesa · 2019
Cited alongside, same era.
Making convolutional networks shift-invariant again
R. Zhang · 2019
Cited alongside, same era.
MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark
M. Contributors · 2020
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
Cited alongside, same era.
PIC: permutation invariant critic for multi-agent deep reinforcement learning
I.-J. Liu, R. A. Yeh, and A. G. Schwing · 2020
Cited alongside, same era.
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Transformers in vision: A survey
S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah · 2022
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MViTv2: Improved multiscale vision transformers for classification and detection
Y. Li, C.-Y. Wu, H. Fan, K. Mangalam, B. Xiong, J. Malik, and C. Feichtenhofer · 2022
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Swin transformer v2: Scaling up capacity and resolution
Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong, et al · 2022
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SpeqNets: Sparsity-aware permutation-equivariant graph networks
C. Morris, G. Rattan, S. Kiefer, and S. Ravanbakhsh · 2022
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Learnable polyphase sampling for shift invariant and equivariant convolutional networks
R. A. Rojas-Gomez, T. Y. Lim, A. G. Schwing, M. N. Do, and R. A. Yeh · 2022
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Learning partial equivariances from data
D. W. Romero and S. Lohit · 2022
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Relaxing equivariance constraints with non-stationary continuous filters
T. van der Ouderaa, D. W. Romero, and M. van der Wilk · 2022
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Equivariance discovery by learned parameter-sharing
R. A. Yeh, Y.-T. Hu, M. Hasegawa-Johnson, and A. Schwing · 2022
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Reviving shift equivariance in vision transformers
P. Ding, D. Soselia, T. Armstrong, J. Su, and F. Huang · 2023
Closest in time.
Image to sphere: Learning equivariant features for efficient pose prediction
D. M. Klee, O. Biza, R. Platt, and R. Walters · 2023
Closest in time.
Alias-free convnets: Fractional shift invariance via polynomial activations
H. Michaeli, T. Michaeli, and D. Soudry · 2023
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