Machine learningDeep Learning, State Space Models

Vision Mamba

Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.

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Sources

  1. Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning. link

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Referenced by

ScholarGateVision Mamba (Vision Mamba: Efficient State Space Models for Image Understanding). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/vision-mamba