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The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks.
Signals and systems
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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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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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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Improving language understanding by generative pre-training
Alec Radford and Karthik Narasimhan · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
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Lucas Beyer, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2020
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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
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2021
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
Rwkv: Reinventing rnns for the transformer era
Bo Peng, Eric Alcaide, Quentin Gregory Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Nguyen Chung, Leon Derczynski, et al · 2023
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Exploring stochastic autoregressive image modeling for visual representation
Yu Qi, Fan Yang, Yousong Zhu, Yufei Liu, Liwei Wu, Rui Zhao, and Wei Li · 2023
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Rejuvenating image-gpt as strong visual representation learners
Sucheng Ren, Zeyu Wang, Hongru Zhu, Junfei Xiao, Alan Yuille, and Cihang Xie · 2023
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Tinymim: An empirical study of distilling mim pre-trained models
Sucheng Ren, Fangyun Wei, Zheng Zhang, and Han Hu · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 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.
Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah A Smith, and Lingpeng Kong · 2021
Cited alongside, same era.
BEiT: BERT pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2022
Cited alongside, same era.
How to train your hippo: State space models with generalized orthogonal basis projections
Albert Gu, Isys Johnson, Aman Timalsina, Atri Rudra, and Christopher Re · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Cited alongside, same era.
Self-supervision through random segments with autoregressive coding (randsac)
Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, and Leonid Sigal · 2022
Cited alongside, same era.
Later among the works it cites.
Scalable pre-training of large autoregressive image models
Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai, Miguel Angel Bautista, Alexander Toshev, Vaishaal Shankar, Joshua M Susskind, and Armand Joulin · 2024
Closest in time.
Mambair: A simple baseline for image restoration with state-space model
Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia · 2024
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Localmamba: Visual state space model with windowed selective scan
Tao Huang, Xiaohuan Pei, Shan You, Fei Wang, Chen Qian, and Chang Xu · 2024
Closest in time.
Mamba-nd: Selective state space modeling for multi-dimensional data
Shufan Li, Harkanwar Singh, and Aditya Grover · 2024
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Swin-umamba: Mamba-based unet with imagenet-based pretraining
Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, et al · 2024
Closest in time.
Vmamba: Visual state space model
Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu · 2024
Closest in time.
Efficientvmamba: Atrous selective scan for light weight visual mamba
Xiaohuan Pei, Tao Huang, and Chang Xu · 2024
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Eagle and finch: Rwkv with matrix-valued states and dynamic recurrence
Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Teddy Ferdinan, Haowen Hou, Przemysław Kazienko, et al · 2024
Closest in time.
Mamba-r: Vision mamba also needs registers
Feng Wang, Jiahao Wang, Sucheng Ren, Guoyizhe Wei, Jieru Mei, Wei Shao, Yuyin Zhou, Alan Yuille, and Cihang Xie · 2024
Closest in time.
Mamba-unet: Unet-like pure visual mamba for medical image segmentation
Ziyang Wang, Jian-Qing Zheng, Yichi Zhang, Ge Cui, and Lei Li · 2024
Closest in time.
Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation
Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, and Lei Zhu · 2024
Closest in time.
Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang · 2024
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