Built on

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NeurIPS , 2012, pp. 1106–1114
2012
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, “Microsoft coco: Common objects in context,” in ECCV , 2014, pp. 740–755
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” arXiv , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. Berseth, “Isic 2017 - skin lesion analysis towards melanoma detection,” arXiv preprint arXiv:1703.00523 , 2017
Original
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in CVPR , 2017, pp. 77–85
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in ICCV , 2017, pp. 618–626
2017
Earlier work this paper cites.
O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P.-A. Heng, I. Cetin, K. Lekadir, O. Camara, M. A. Gonzalez Ballester, G. Sanroma, S. Napel, S. Petersen, G. Tziritas, E. Grinias, M. Khened, V. A. Kollerathu, G. Krishnamurthi, M.-M. Rohé, X. Pennec, M. Sermesant, F. Isensee, P. J?ger, K. H. Maier-Hein, P. M. Full, I. Wolf, S. Engelhardt, C. F. Baumgartner, L. M. Koch, J. M. Wolterink, I. I?gum, Y. Jang, Y. Hong, J. Patravali, S. Jain, O. Humbert, and P.-M. Jodoin, “Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?” IEEE Transactions on Medical Imaging , vol. 37, no. 11, pp. 2514–2525, 2018
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-low-shotitation networks,” in CVPR , 2018, pp. 7132–7141
2018
Earlier work this paper cites.
Hu, Jie and Shen, Li and Sun, Gang, “Squeeze-and-excitation networks,” in CVPR , 2018, pp. 7132–7141
2018
Earlier work this paper cites.
A. Myronenko, “3d mri brain tumor segmentation using autoencoder regularization,” in BrainLes@MICCAI , 2018
2018
Earlier work this paper cites.
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” in ECCV , 2018, pp. 418–434
2018
Earlier work this paper cites.
M. Allan, A. A. Shvets, T. Kurmann, Z. Zhang, R. Duggal, Y.-H. Su, N. Rieke, I. Laina, N. Kalavakonda, S. Bodenstedt, L. C. García-Peraza, W. Li, V. I. Iglovikov, H. Luo, J. Yang, D. Stoyanov, L. Maier-Hein, S. Speidel, and M. Azizian, “2017 robotic instrument segmentation challenge,” arXiv , 2019
2019
Earlier work this paper cites.
N. C. F. Codella, V. M. Rotemberg, P. Tschandl, M. E. Celebi, S. W. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. A. Marchetti, H. Kittler, and A. C. Halpern, “Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic),” arXiv preprint arXiv:1902.03368 , 2019
Original
2019
Earlier work this paper cites.
H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” in ICLR , 2019, pp. 1–13
2019
Earlier work this paper cites.
M. Tan and Q. V. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in ICML , 2019, pp. 6105–6114
2019
Earlier work this paper cites.
H. X. T. S. A. A. Zhou, BoleiZhao, “Semantic understanding of scenes through the ade20k dataset,” International Journal of Computer Vision , pp. 302–321, 2019
2019
Earlier work this paper cites.
A. Gu, T. Dao, S. Ermon, A. Rudra, and C. Ré, “Hippo: Recurrent memory with optimal polynomial projections,” in NeurIPS , 2020, pp. 1474–1487
2020
Earlier work this paper cites.
F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods , vol. 18, pp. 203 – 211, 2020
2020
Earlier work this paper cites.
I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, and P. Dollar, “Designing network design spaces,” in CVPR , 2020
2020
Earlier work this paper cites.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. J’egou, “Training data-efficient image transformers & distillation through attention,” in ICML , 2020, pp. 10 347–10 357
2020
Earlier work this paper cites.
C. Yang, Z. Chen, M. Espinosa, L. Ericsson, Z. Wang, J. Liu, and E. J. Crowley, “Plainmamba: Improving non-hierarchical mamba in visual recognition,” arXiv preprint arXiv:2403.17695 , 2020
Original
2020
Earlier work this paper cites.
A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, and C. Schmid, “Vivit: A video vision transformer,” in ICCV , 2021, pp. 6836–6846
2021
Earlier work this paper cites.
G. Bertasius, H. Wang, and L. Torresani, “Is space-time attention all you need for video understanding?” arXiv preprint arXiv:2102.05095 , 2021
Original
2021
Earlier work this paper cites.
H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmentation,” in ECCV , 2021
2021
Earlier work this paper cites.
A. Dosovitskiy, et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021, pp. 1–16
2021
Earlier work this paper cites.
A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. Ré, “Combining recurrent, convolutional, and continuous-time models with linear state space layers,” in NeurIPS , 2021, pp. 572–585
2021
Earlier work this paper cites.
A. Hatamizadeh, D. Yang, H. R. Roth, and D. Xu, “Unetr: Transformers for 3d medical image segmentation,” WACV , pp. 1748–1758, 2021
2021
Earlier work this paper cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in ICCV , 2021, pp. 9992–10 002
2021
Earlier work this paper cites.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pvt v2: Improved baselines with pyramid vision transformer,” Computational Visual Media , pp. 415–424, 2021
2021
Earlier work this paper cites.
Y. Zhang, H. Liu, and Q. Hu, “Transfuse: Fusing transformers and cnns for medical image segmentation,” arXiv , 2021
2021
Earlier work this paper cites.
H.-Y. Zhou, J. Guo, Y. Zhang, L. Yu, L. Wang, and Y. Yu, “nnformer: Volumetric medical image segmentation via a 3d transformer,” IEEE TIP , vol. 32, pp. 4036–4045, 2021
2021
Earlier work this paper cites.
Z. L. andHanzi Mao, C. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in CVPR , 2022, pp. 11 966–11 976
2022
Earlier work this paper cites.
Y. Gao, M. Zhou, D. Liu, and D. N. Metaxas, “A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks,” arXiv preprint arXiv:2203.00131 , 2022
Original
2022
Earlier work this paper cites.
A. Gu, K. Goel, and C. Ré, “Efficiently modeling long sequences with structured state spaces,” in ICLR , 2022, pp. 1–27
2022
Earlier work this paper cites.
A. Gupta, A. Gu, and J. Berant, “Diagonal state spaces are as effective as structured state spaces,” arXiv preprint arXiv:2203.14343 , 2022
Original
2022
Earlier work this paper cites.
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, “Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,” arXiv preprint arXiv:2201.01266 , 2022
Original
2022
Earlier work this paper cites.
Y. Ji, H. Bai, J. Yang, C. Ge, Y. Zhu, R. Zhang, Z. Li, L. Zhang, W. Ma, X. Wan, and P. Luo, “Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,” arXiv preprint arXiv:2206.08023 , 2022
Original
2022
Earlier work this paper cites.
Y. Li, T. Cai, Y. Zhang, D. Chen, and D. Dey, “What makes convolutional models great on long sequence modeling?” arXiv preprint arXiv:2210.09298 , 2022
Original
2022
Earlier work this paper cites.
J. Ruan, S. Xiang, M. Xie, T. Liu, and Y. Fu, “Malunet: A multi-attention and light-weight unet for skin lesion segmentation,” BIBM , pp. 1150–1156, 2022
2022
Earlier work this paper cites.
W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, J. Feng, and S. Yan, “Metaformer is actually what you need for vision,” in CVPR , 2022, pp. 10 819–10 829
2022
Earlier work this paper cites.
Y. Cai, H. Bian, J. Lin, H. Wang, R. Timofte, and Y. Zhang, “Retinexformer: One-stage retinex-based transformer for low-light image enhancement,” in ICCV , 2023, pp. 12 470–12 479
2023
Earlier work this paper cites.
A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,” arXiv preprint arXiv:2312.00752 , 2023
Original
2023
Earlier work this paper cites.
M. M. Islam, M. Hasan, K. S. Athrey, T. Braskich, and G. Bertasius, “Efficient movie scene detection using state-space transformers,” in CVPR , 2023, pp. 18 749–18 758
2023
Earlier work this paper cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” in NeurIPS , vol. 36, 2023, pp. 34 892–34 916
2023
Earlier work this paper cites.

Similar

A. Orvieto, S. L. Smith, A. Gu, A. Fernando, C. Gulcehre, R. Pascanu, and S. De, “Resurrecting recurrent neural networks for long sequences,” arXiv preprint arXiv:2303.06349 , 2023
Original
2023
Cited alongside, same era.
B. Zou, Z. Guo, X. Hu, and H. Ma, “Rhythmmamba: Fast remote physiological measurement with arbitrary length videos,” arXiv preprint arXiv:2404.06483 , 2024
Original
2023
Cited alongside, same era.
B. N. Patro and Vijay S. Agneeswaran, “Simba: Simplified mamba-based architecture for vision and multivariate time series,” arXiv preprint arXiv:2403.15360 , 2024
Original
2024
Cited alongside, same era.
J. Bai, Y. Yin, and Q. He, “Retinexmamba: Retinex-based mamba for low-light image enhancement,” arXiv preprint arXiv:2405.03349 , 2024
Original
2024
Cited alongside, same era.

Then

M. Oquab et al. , “Dinov2: Learning robust visual features without supervision,” arXiv preprint arXiv:2304.07193 , 2024
Original
2024
Closest in time.
B. N. Patro and V. S. Agneeswaran, “Mamba-360: Survey of state space models as transformer alternative for long sequence modelling: Methods, applications, and challenges,” arXiv , 2024
2024
Closest in time.
S. Peng, X. Zhu, H. Deng, Z. Lei, and L.-J. Deng, “Fusionmamba: Efficient image fusion with state space model,” arXiv , 2024
2024
Closest in time.
Z. Qian and Z. Xiao, “Smcd: High realism motion style transfer via mamba-based diffusion,” arXiv , 2024
2024
Closest in time.
Y. Qiao, Z. Yu, L. Guo, S. Chen, Z. Zhao, M. Sun, Q. Wu, and J. Liu, “Vl-mamba: Exploring state space models for multimodal learning,” arXiv preprint arXiv:2403.13600 , 2024
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2024

Vision Mamba: A Comprehensive Survey and Taxonomy

Liu, Xiao, Zhang, Chenxu, Zhang, Lei

Understand

State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems.

This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning.
In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding.
By mapping sequence data to state space, long-term dependencies in the data can be better captured.

Built on

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NeurIPS , 2012, pp. 1106–1114
2012
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, “Microsoft coco: Common objects in context,” in ECCV , 2014, pp. 740–755
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” arXiv , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. Berseth, “Isic 2017 - skin lesion analysis towards melanoma detection,” arXiv preprint arXiv:1703.00523 , 2017
Original
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in CVPR , 2017, pp. 77–85
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in ICCV , 2017, pp. 618–626
2017
Earlier work this paper cites.
O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P.-A. Heng, I. Cetin, K. Lekadir, O. Camara, M. A. Gonzalez Ballester, G. Sanroma, S. Napel, S. Petersen, G. Tziritas, E. Grinias, M. Khened, V. A. Kollerathu, G. Krishnamurthi, M.-M. Rohé, X. Pennec, M. Sermesant, F. Isensee, P. J?ger, K. H. Maier-Hein, P. M. Full, I. Wolf, S. Engelhardt, C. F. Baumgartner, L. M. Koch, J. M. Wolterink, I. I?gum, Y. Jang, Y. Hong, J. Patravali, S. Jain, O. Humbert, and P.-M. Jodoin, “Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?” IEEE Transactions on Medical Imaging , vol. 37, no. 11, pp. 2514–2525, 2018
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-low-shotitation networks,” in CVPR , 2018, pp. 7132–7141
2018
Earlier work this paper cites.
Hu, Jie and Shen, Li and Sun, Gang, “Squeeze-and-excitation networks,” in CVPR , 2018, pp. 7132–7141
2018
Earlier work this paper cites.
A. Myronenko, “3d mri brain tumor segmentation using autoencoder regularization,” in BrainLes@MICCAI , 2018
2018
Earlier work this paper cites.
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” in ECCV , 2018, pp. 418–434
2018
Earlier work this paper cites.
M. Allan, A. A. Shvets, T. Kurmann, Z. Zhang, R. Duggal, Y.-H. Su, N. Rieke, I. Laina, N. Kalavakonda, S. Bodenstedt, L. C. García-Peraza, W. Li, V. I. Iglovikov, H. Luo, J. Yang, D. Stoyanov, L. Maier-Hein, S. Speidel, and M. Azizian, “2017 robotic instrument segmentation challenge,” arXiv , 2019
2019
Earlier work this paper cites.
N. C. F. Codella, V. M. Rotemberg, P. Tschandl, M. E. Celebi, S. W. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. A. Marchetti, H. Kittler, and A. C. Halpern, “Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic),” arXiv preprint arXiv:1902.03368 , 2019
Original
2019
Earlier work this paper cites.
H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” in ICLR , 2019, pp. 1–13
2019
Earlier work this paper cites.
M. Tan and Q. V. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in ICML , 2019, pp. 6105–6114
2019
Earlier work this paper cites.
H. X. T. S. A. A. Zhou, BoleiZhao, “Semantic understanding of scenes through the ade20k dataset,” International Journal of Computer Vision , pp. 302–321, 2019
2019
Earlier work this paper cites.
A. Gu, T. Dao, S. Ermon, A. Rudra, and C. Ré, “Hippo: Recurrent memory with optimal polynomial projections,” in NeurIPS , 2020, pp. 1474–1487
2020
Earlier work this paper cites.
F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods , vol. 18, pp. 203 – 211, 2020
2020
Earlier work this paper cites.
I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, and P. Dollar, “Designing network design spaces,” in CVPR , 2020
2020
Earlier work this paper cites.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. J’egou, “Training data-efficient image transformers & distillation through attention,” in ICML , 2020, pp. 10 347–10 357
2020
Earlier work this paper cites.
C. Yang, Z. Chen, M. Espinosa, L. Ericsson, Z. Wang, J. Liu, and E. J. Crowley, “Plainmamba: Improving non-hierarchical mamba in visual recognition,” arXiv preprint arXiv:2403.17695 , 2020
Original
2020
Earlier work this paper cites.
A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, and C. Schmid, “Vivit: A video vision transformer,” in ICCV , 2021, pp. 6836–6846
2021
Earlier work this paper cites.
G. Bertasius, H. Wang, and L. Torresani, “Is space-time attention all you need for video understanding?” arXiv preprint arXiv:2102.05095 , 2021
Original
2021
Earlier work this paper cites.
H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmentation,” in ECCV , 2021
2021
Earlier work this paper cites.
A. Dosovitskiy, et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021, pp. 1–16
2021
Earlier work this paper cites.
A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. Ré, “Combining recurrent, convolutional, and continuous-time models with linear state space layers,” in NeurIPS , 2021, pp. 572–585
2021
Earlier work this paper cites.
A. Hatamizadeh, D. Yang, H. R. Roth, and D. Xu, “Unetr: Transformers for 3d medical image segmentation,” WACV , pp. 1748–1758, 2021
2021
Earlier work this paper cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in ICCV , 2021, pp. 9992–10 002
2021
Earlier work this paper cites.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pvt v2: Improved baselines with pyramid vision transformer,” Computational Visual Media , pp. 415–424, 2021
2021
Earlier work this paper cites.
Y. Zhang, H. Liu, and Q. Hu, “Transfuse: Fusing transformers and cnns for medical image segmentation,” arXiv , 2021
2021
Earlier work this paper cites.
H.-Y. Zhou, J. Guo, Y. Zhang, L. Yu, L. Wang, and Y. Yu, “nnformer: Volumetric medical image segmentation via a 3d transformer,” IEEE TIP , vol. 32, pp. 4036–4045, 2021
2021
Earlier work this paper cites.
Z. L. andHanzi Mao, C. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in CVPR , 2022, pp. 11 966–11 976
2022
Earlier work this paper cites.
Y. Gao, M. Zhou, D. Liu, and D. N. Metaxas, “A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks,” arXiv preprint arXiv:2203.00131 , 2022
Original
2022
Earlier work this paper cites.
A. Gu, K. Goel, and C. Ré, “Efficiently modeling long sequences with structured state spaces,” in ICLR , 2022, pp. 1–27
2022
Earlier work this paper cites.
A. Gupta, A. Gu, and J. Berant, “Diagonal state spaces are as effective as structured state spaces,” arXiv preprint arXiv:2203.14343 , 2022
Original
2022
Earlier work this paper cites.
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, “Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,” arXiv preprint arXiv:2201.01266 , 2022
Original
2022
Earlier work this paper cites.
Y. Ji, H. Bai, J. Yang, C. Ge, Y. Zhu, R. Zhang, Z. Li, L. Zhang, W. Ma, X. Wan, and P. Luo, “Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,” arXiv preprint arXiv:2206.08023 , 2022
Original
2022
Earlier work this paper cites.
Y. Li, T. Cai, Y. Zhang, D. Chen, and D. Dey, “What makes convolutional models great on long sequence modeling?” arXiv preprint arXiv:2210.09298 , 2022
Original
2022
Earlier work this paper cites.
J. Ruan, S. Xiang, M. Xie, T. Liu, and Y. Fu, “Malunet: A multi-attention and light-weight unet for skin lesion segmentation,” BIBM , pp. 1150–1156, 2022
2022
Earlier work this paper cites.
W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, J. Feng, and S. Yan, “Metaformer is actually what you need for vision,” in CVPR , 2022, pp. 10 819–10 829
2022
Earlier work this paper cites.
Y. Cai, H. Bian, J. Lin, H. Wang, R. Timofte, and Y. Zhang, “Retinexformer: One-stage retinex-based transformer for low-light image enhancement,” in ICCV , 2023, pp. 12 470–12 479
2023
Earlier work this paper cites.
A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,” arXiv preprint arXiv:2312.00752 , 2023
Original
2023
Earlier work this paper cites.
M. M. Islam, M. Hasan, K. S. Athrey, T. Braskich, and G. Bertasius, “Efficient movie scene detection using state-space transformers,” in CVPR , 2023, pp. 18 749–18 758
2023
Earlier work this paper cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” in NeurIPS , vol. 36, 2023, pp. 34 892–34 916
2023
Earlier work this paper cites.

Similar

A. Orvieto, S. L. Smith, A. Gu, A. Fernando, C. Gulcehre, R. Pascanu, and S. De, “Resurrecting recurrent neural networks for long sequences,” arXiv preprint arXiv:2303.06349 , 2023
Original
2023
Cited alongside, same era.
B. Zou, Z. Guo, X. Hu, and H. Ma, “Rhythmmamba: Fast remote physiological measurement with arbitrary length videos,” arXiv preprint arXiv:2404.06483 , 2024
Original
2023
Cited alongside, same era.
B. N. Patro and Vijay S. Agneeswaran, “Simba: Simplified mamba-based architecture for vision and multivariate time series,” arXiv preprint arXiv:2403.15360 , 2024
Original
2024
Cited alongside, same era.
J. Bai, Y. Yin, and Q. He, “Retinexmamba: Retinex-based mamba for low-light image enhancement,” arXiv preprint arXiv:2405.03349 , 2024
Original
2024
Cited alongside, same era.

Then

M. Oquab et al. , “Dinov2: Learning robust visual features without supervision,” arXiv preprint arXiv:2304.07193 , 2024
Original
2024
Closest in time.
B. N. Patro and V. S. Agneeswaran, “Mamba-360: Survey of state space models as transformer alternative for long sequence modelling: Methods, applications, and challenges,” arXiv , 2024
2024
Closest in time.
S. Peng, X. Zhu, H. Deng, Z. Lei, and L.-J. Deng, “Fusionmamba: Efficient image fusion with state space model,” arXiv , 2024
2024
Closest in time.
Z. Qian and Z. Xiao, “Smcd: High realism motion style transfer via mamba-based diffusion,” arXiv , 2024
2024
Closest in time.
Y. Qiao, Z. Yu, L. Guo, S. Chen, Z. Zhao, M. Sun, Q. Wu, and J. Liu, “Vl-mamba: Exploring state space models for multimodal learning,” arXiv preprint arXiv:2403.13600 , 2024
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Z. Cao, X. Wu, L.-J. Deng, Y. Zhong, and S. A. Novel, “A novel state space model with local enhancement and state sharing for image fusion,” arXiv preprint arXiv:2404.09293 , 2024

Original

2024

Cited alongside, same era.

C.-S. Chen, G.-Y. Chen, D. Zhou, D. Jiang, and D.-S. Chen, “Res-vmamba: Fine-grained food category visual classification using selective state space models with deep residual learning,” arXiv preprint arXiv:2402.15761 , 2024

Original

2024

Cited alongside, same era.

G. Chen, Y. Huang, J. Xu, B. Pei, Z. Chen, Z. Li, J. Wang, K. Li, T. Lu, and L. Wang, “Video mamba suite: State space model as a versatile alternative for video understanding,” arXiv preprint arXiv:2403.09626 , 2024

Original

2024

Cited alongside, same era.

H. Chen, J. Song, C. Han, J. Xia, and N. Yokoya, “Changemamba: Remote sensing change detection with spatio-temporal state space model,” arXiv preprint arXiv:2404.03425 , 2024

Original

2024

Cited alongside, same era.

K. Chen, B. Chen, C. Liu, W. Li, Z. Zou, and Z. Shi, “Rsmamba: Remote sensing image classification with state space model,” arXiv preprint arXiv:2404.01705 , 2024

Original

2024

Cited alongside, same era.

T. Chen, Z. Tan, T. Gong, Q. Chu, Y. Wu, B. Liu, J. Ye, and N. Yu, “Mim-istd: Mamba-in-mamba for efficient infrared small target detection,” arXiv preprint arXiv:2403.02148 , 2024

Original

2024

Cited alongside, same era.

Y. Chen, J. Xie, Y. Lin, Y. Song, W. Yang, and R. Yu, “Survmamba: State space model with multi-grained multi-modal interaction for survival prediction,” arXiv preprint arXiv:2404.08027 , 2024

Original

2024

Cited alongside, same era.

C. Cheng, H. Wang, and H. Sun, “Activating wider areas in image super-resolution,” arXiv preprint arXiv:2403.08330 , 2024

Original

2024

Cited alongside, same era.

2024

Closest in time.

Renkai Wu and Yinghao Liu and Pengchen Liang and Qing Chang, “H-vmunet: High-order vision mamba unet for medical image segmentation,” arXiv preprint arXiv:2403.13642 , 2024

Original

2024

Closest in time.

J. Ruan and S. Xiang, “Vm-unet: Vision mamba unet for medical image segmentation,” arXiv preprint arXiv:2402.02491 , 2024

Original

2024

Closest in time.

K. S. Sanjid, M. T. Hossain, M. S. S. Junayed, and D. M. M. Uddin, “Integrating mamba sequence model and hierarchical upsampling network for accurate semantic segmentation of multiple sclerosis legion,” arXiv preprint arXiv:2403.17432 , 2024

Original

2024

Closest in time.

Q. Shen, X. Yi, Z. Wu, P. Zhou, H. Zhang, S. Yan, and X. Wang, “Gamba: Marry gaussian splatting with mamba for single view 3d reconstruction,” arXiv preprint arXiv:2403.18795 , 2024

Original

2024

Closest in time.

Y. Shi, B. Xia, X. Jin, X. Wang, T. Zhao, X. Xia, X. Xiao, and W. Yang, “Vmambair: Visual state space model for image restoration,” arXiv preprint arXiv:2403.11423 , 2024

Original

2024

Closest in time.

H. Tang, L. Cheng, G. Huang, Z. Tan, J. Lu, and K. Wu, “Rotate to scan: Unet-like mamba with triplet ssm module for medical image segmentation,” arXiv preprint arXiv:2403.17701 , 2024

Original

2024

Closest in time.

Z. Wan, Y. Wang, S. Yong, P. Zhang, S. Stepputtis, K. P. Sycara, and Y. Xie, “Sigma: Siamese mamba network for multi-modal semantic segmentation,” arXiv preprint arXiv:2404.04256 , 2024

Original

2024

Closest in time.

G. Wang, X. Zhang, Z. Peng, T. Zhang, X. Jia, and L. Jiao, “S2mamba: A spatial-spectral state space model for hyperspectral image classification,” arXiv preprint arXiv:2404.18213 , 2024

Original

2024

Closest in time.

J. Wang, J. Chen, D. Chen, and J. Wu, “Large window-based mamba unet for medical image segmentation: Beyond convolution and self-attention,” arXiv preprint arXiv:2403.07332 , 2024

Original

2024

Closest in time.

Q. Wang, H. Hu, and Y. Zhou, “Memorymamba: Memory-augmented state space model for defect recognition,” arXiv preprint arXiv:2405.03673 , 2024

Original

2024

Closest in time.

Q. Wang, C. Wang, Z. Lai, and Y. Zhou, “Insectmamba: Insect pest classification with state space model,” arXiv , 2024

2024

Closest in time.

X. Wang, S. Wang, Y. Ding, Y. Li, W. Wu, Y. Rong, W. Kong, J. Huang, S. Li, H. Yang, Z. Wang, B. Jiang, C. Li, Y. Wang, Y. Tian, and J. Tang, “State space model for new-generation network alternative to transformers: A survey,” arXiv , 2024

2024

Closest in time.

Z. Wang and C. Ma, “Weak-mamba-unet: Visual mamba makes cnn and vit work better for scribble-based medical image segmentation,” arXiv preprint arXiv:2402.10887 , 2024

Original

2024

Closest in time.

Z. Wang, J.-Q. Zheng, C. Ma, and T. Guo, “Vmambamorph: a multi-modality deformable image registration framework based on visual state space model with cross-scan module,” arXiv preprint arXiv:2404.05105 , 2024

Original

2024

Closest in time.

Z. Wang, J.-Q. Zheng, Y. Zhang, G. Cui, and L. Li, “Mamba-unet: Unet-like pure visual mamba for medical image segmentation,” arXiv preprint arXiv:2402.05079 , 2024

Original

2024

Closest in time.

R. Wu, Y. Liu, P. Liang, and Q. Chang, “Ultralight vm-unet: Parallel vision mamba significantly reduces parameters for skin lesion segmentation,” arXiv preprint arXiv:2403.20035 , 2024

Original

2024

Closest in time.

P. Xiaohuan, T. Huang, and C. Xu, “Efficientvmamba: Atrous selective scan for light weight visual mamba,” arXiv , 2024

2024

Closest in time.

J. Xie, R. Liao, Z. Zhang, S. Yi, Y. Zhu, and G. Luo, “Promamba: Prompt-mamba for polyp segmentation,” arXiv , 2024

2024

Closest in time.

X. Xie, Y. Cui, C.-I. Ieong, T. Tan, X. Zhang, X. Zheng, and Z. Yu, “Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba,” arXiv preprint arXiv:2404.09498 , 2024

Original

2024

Closest in time.

Z. Xing, T. Ye, Y. Yang, G. Liu, and L. Zhu, “Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,” arXiv preprint arXiv:2401.13560 , 2024

Original

2024

Closest in time.

R. Xu, S. Yang, Y. Wang, and B. Du, “A survey on vision mamba: Models, applications and challenges,” arXiv , 2024

2024

Closest in time.

Z. Xu, Y. Lin, H. Han, S. Yang, R. Li, Y. Zhang, and X. Li, “Mambatalk: Efficient holistic gesture synthesis with selective state space models,” arXiv preprint arXiv:2403.09471 , 2024

Original

2024

Closest in time.

G. Yang, K. Du, Z. Yang, Y. Du, and S. W. Yongping Zheng, “Cmvim: Contrastive masked vim autoencoder for 3d multi-modal representation learning for ad classification,” arXiv , 2024

2024

Closest in time.

S. Yang, Y. Wang, and H. Chen, “Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology,” arXiv preprint arXiv:2403.06800 , 2024

Original

2024

Closest in time.

Y. Yang, Z. Xing, C. Huang, and L. Zhu, “Vivim: a video vision mamba for medical video object segmentation,” arXiv , 2024

2024

Closest in time.

Y. Yang, C. Ma, J. Yao, Z. Zhong, Y. Zhang, and Y. Wang, “Remamber: Referring image segmentation with mamba twister,” arXiv preprint arXiv:2403.17839 , 2024

Original

2024

Closest in time.

J. Yao, D. Hong, C. Li, and J. Chanussot, “Spectralmamba: Efficient mamba for hyperspectral image classification,” arXiv preprint arXiv:2404.08489 , 2024

Original

2024

Closest in time.

Z. Ye, T. Chen, F. Wang, H. Zhang, G. Li, and L. Zhang, “P-mamba: Marrying perona malik diffusion with mamba for efficient pediatric echocardiographic left ventricular segmentation,” arXiv preprint arXiv:2402.08506 , 2024

Original

2024

Closest in time.

Y. Yue and Z. Li, “Medmamba: Vision mamba for medical image classification,” arXiv preprint arXiv:2403.03849 , 2024

Original

2024

Closest in time.

T. Zhan, X. Li, H. Yuan, S. Ji, and S. Yan, “Point cloud mamba: Point cloud learning via state space model,” arXiv , 2024

2024

Closest in time.

H. Zhang, Y. Zhu, D. Wang, L. Zhang, T. Chen, and Z. Ye, “A survey on visual mamba,” arXiv preprint arXiv:2404.15956 , 2024

Original

2024

Closest in time.

M. Zhang, Y. Yu, L. Gu, T. Lin, and X. Tao, “Vm-unet-v2 rethinking vision mamba unet for medical image segmentation,” arXiv preprint arXiv:2403.09157 , 2024

Original

2024

Closest in time.

Y. Zhang, W. Yan, K. Yan, C. Lam, Y. Qiu, P. Zheng, R. Tang, and S. Cheng, “Motion-guided dual-camera tracker for low-cost skill evaluation of gastric endoscopy,” arXiv , 2024

2024

Closest in time.

Z. Zhang, A. Liu, I. Reid, R. Hartley, B. Zhuang, and H. Tang, “Motion mamba: Efficient and long sequence motion generation with hierarchical and bidirectional selective ssm,” arXiv , 2024

2024

Closest in time.

H. Zhao, M. Zhang, W. Zhao, P. Ding, S. Huang, and D. Wang, “Cobra: Extending mamba to multi-modal large language model for efficient inference,” arXiv preprint arXiv:2403.14520 , 2024

Original

2024

Closest in time.

S. Zhao, H. Chen, X.-l. Zhang, P. Xiao, L. Bai, and W. Ouyang, “Rs-mamba for large remote sensing image dense prediction,” arXiv preprint arXiv:2404.02668 , 2024

Original

2024

Closest in time.

Z. Zhen, Y. Hu, and Z. Feng, “Freqmamba: Viewing mamba from a frequency perspective for image deraining,” arXiv , 2024

2024

Closest in time.

Z. Zheng and C. Wu, “U-shaped vision mamba for single image dehazing,” arXiv preprint arXiv:2402.04139 , 2024

Original

2024

Closest in time.

Z. Zheng and J. Zhang, “Fd-vision mamba for endoscopic exposure correction,” arXiv preprint arXiv:2402.06378 , 2024

Original

2024

Closest in time.

L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision mamba: Efficient visual representation learning with bidirectional state space model,” arXiv , 2024

2024

Closest in time.

Q. Zhu, Y. Cai, Y. Fang, Y. Yang, C. Chen, L. Fan, and A. Nguyen, “Samba: Semantic segmentation of remotely sensed images with state space model,” arXiv preprint arXiv:2404.01705 , 2024

Original

2024

Closest in time.