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Vision mambas have demonstrated strong performance with linear complexity to the number of vision tokens.
Long short-term memory
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
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Attention-based ensemble for deep metric learning
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Improving language understanding by generative pre-training
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Unified perceptual parsing for scene understanding
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MMDetection: Open mmlab detection toolbox and benchmark
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Theory of the frequency principle for general deep neural networks
Tao Luo, Zheng Ma, Zhi-Qin John Xu, and Yaoyu Zhang · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Deep asymmetric metric learning via rich relationship mining
Xinyi Xu, Yanhua Yang, Cheng Deng, and Feng Zheng · 2019
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Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
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Language models are few-shot learners
Tom B Brown · 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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MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark, 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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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Learning in the frequency domain
Kai Xu, Minghai Qin, Fei Sun, Yuhao Wang, Yen-Kuang Chen, and Fengbo Ren · 2020
Gfnet: Global filter networks for visual recognition
Yongming Rao, Wenliang Zhao, Zheng Zhu, Jie Zhou, and Jiwen Lu · 2023
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The llama 3 herd of models
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Zigma: Zigzag mamba diffusion model
Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Fischer, and Bjorn Ommer · 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
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Videomamba: State space model for efficient video understanding
Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao · 2024
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Pointmamba: A simple state space model for point cloud analysis
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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
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Fcanet: Frequency channel attention networks
Zequn Qin, Pengyi Zhang, Fei Wu, and Xi Li · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Learning frequency-aware dynamic network for efficient super-resolution
Wenbin Xie, Dehua Song, Chang Xu, Chunjing Xu, Hui Zhang, and Yunhe Wang · 2021
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On the parameterization and initialization of diagonal state space models
Albert Gu, Karan Goel, Ankit Gupta, and Christopher Ré · 2022
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Diagonal state spaces are as effective as structured state spaces
Ankit Gupta, Albert Gu, and Jonathan Berant · 2022
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Dingkang Liang, Xin Zhou, Xinyu Wang, Xingkui Zhu, Wei Xu, Zhikang Zou, Xiaoqing Ye, and Xiang Bai · 2024
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Jamba: A hybrid transformer-mamba language model
Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, et al · 2024
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Vmamba: Visual state space model
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U-mamba: Enhancing long-range dependency for biomedical image segmentation
Jun Ma, Feifei Li, and Bo Wang · 2024
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Simba: Simplified mamba-based architecture for vision and multivariate time series
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Efficientvmamba: Atrous selective scan for light weight visual mamba
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Visual autoregressive modeling: Scalable image generation via next-scale prediction
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Overview frequency principle/spectral bias in deep learning
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Plainmamba: Improving non-hierarchical mamba in visual recognition
Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson, Zhenyu Wang, Jiaming Liu, and Elliot J Crowley · 2024
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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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