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A central objective in computer vision is to design models with appropriate 2-D inductive bias.
Multidimensional linear iterative circuits—general properties
Donald D Givone and Robert P Roesser · 1972
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New results in 2-d systems theory, part ii: 2-d state-space models—realization and the notions of controllability, observability, and minimality
Sun-Yuan Kung, B.C. Levy, M. Morf, and T. Kailath · 1977
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Realization and stabilization of 2-d systems
Rikus Eising · 1978
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Doubly-indexed dynamical systems: State-space models and structural properties
Ettore Fornasini and Giovanni Marchesini · 1978
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Ts Hinamoto · 1980
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N Vapnik and A Ya Chervonenkis · 2015
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On the expressive power of deep learning: A tensor analysis
Nadav Cohen, Or Sharir, and Amnon Shashua · 2016
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Character-level question answering with attention
David Golub and Xiaodong He · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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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
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Augment your batch: Improving generalization through instance repetition
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2020
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Long range arena: A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 2020
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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On the parameterization and initialization of diagonal state space models
Albert Gu, Ankit Gupta, Karan Goel, and Christopher Ré · 2022
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Diagonal state spaces are as effective as structured state spaces
Ankit Gupta · 2022
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Simplifying and understanding state space models with diagonal linear rnns
Ankit Gupta, Harsh Mehta, and Jonathan Berant · 2022
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Efficient movie scene detection using state-space transformers
Md Mohaiminul Islam, Mahmudul Hasan, Kishan Shamsundar Athrey, Tony Braskich, and Gedas Bertasius · 2022
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Uniformer: Unifying convolution and self-attention for visual recognition, 2022
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An image is worth 16x16 words: Transformers for image recognition at scale
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Efficiently modeling long sequences with structured state spaces
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
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Vision transformer for small-size datasets
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Kunchang Li, Yali Wang, Junhao Zhang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao · 2022
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What makes convolutional models great on long sequence modeling?
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A convnet for the 2020s
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Mega: moving average equipped gated attention
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Long range language modeling via gated state spaces
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S4nd: Modeling images and videos as multidimensional signals using state spaces
Eric Nguyen, Karan Goel, Albert Gu, Gordon W Downs, Preey Shah, Tri Dao, Stephen A Baccus, and Christopher Ré · 2022
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Simplified state space layers for sequence modeling
Jimmy TH Smith, Andrew Warrington, and Scott W Linderman · 2022
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Efficient long sequence modeling via state space augmented transformer
Simiao Zuo, Xiaodong Liu, Jian Jiao, Denis Charles, Eren Manavoglu, Tuo Zhao, and Jianfeng Gao · 2022
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Simple hardware-efficient long convolutions for sequence modeling
Daniel Y Fu, Elliot L Epstein, Eric Nguyen, Armin W Thomas, Michael Zhang, Tri Dao, Atri Rudra, and Christopher Ré · 2023
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Modelling long range dependencies in nd: From task-specific to a general purpose cnn
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Diagonal state space augmented transformers for speech recognition
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