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State-space models (SSMs) have recently shown promise in capturing long-range dependencies with subquadratic computational complexity, making them attractive for various applications.
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Attention is all you need
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2020
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Xiangxiang Chu et al · 2021
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Alexey Dosovitskiy et al · 2021
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Hao Zhang et al · 2022
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Segment anything
Alexander Kirillov et al · 2023
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Sachin Mehta and Mohammad Rastegari · 2023
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Abdelrahman Shaker et al · 2023
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Simplified state space layers for sequence modeling
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Convit: Improving vision transformers with soft convolutional inductive biases
Stéphane d’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun · 2021
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Multiscale vision transformers
Haoqi Fan et al · 2021
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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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Conditional detr for fast training convergence
Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, and Jingdong Wang · 2021
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Bottleneck transformers for visual recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, and Ashish Vaswani · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herve Jegou · 2021
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Convnext v2: Co-designing and scaling convnets with masked autoencoders
Sanghyun Woo et al · 2023
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Video mamba suite: State space model as a versatile alternative for video understanding
Guo Chen, Yifei Huang, Jilan Xu, Baoqi Pei, Zhe Chen, Zhiqi Li, Jiahao Wang, Kunchang Li, Tong Lu, and Limin Wang · 2024
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Haifan Gong, Luoyao Kang, Yitao Wang, Xiang Wan, and Haofeng Li · 2024
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Pan-mamba: Effective pan-sharpening with state space model
Xuanhua He, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, and Man Zhou · 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
Dingkang Liang, Xin Zhou, Xinyu Wang, Xingkui Zhu, Wei Xu, Zhikang Zou, Xiaoqing Ye, and Xiang Bai · 2024
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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
Badri N. Patro and Vijay S. Agneeswaran · 2024
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Efficientvmamba: Atrous selective scan for light weight visual mamba
Xiaohuan Pei, Tao Huang, and Chang Xu · 2024
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Vm-unet: Vision mamba unit for medical image segmentation
Jiacheng Ruan and Suncheng Xiang · 2024
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Efficient video object segmentation via modulated cross-attention memory
Abdelrahman Shaker et al · 2024
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Graph-mamba: Towards long-range graph sequence modeling with selective state spaces
Chloe Wang, Oleksii Tsepa, Jun Ma, and Bo Wang · 2024
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Vivim: a video vision mamba for medical video object segmentation
Yijun Yang, Zhaohu Xing, and Lei Zhu · 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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