  Mamba, a recent selective structured state space model, excels in long
sequence modeling, which is vital in the large model era. Long sequence
modeling poses significant challenges, including capturing long-range
dependencies within the data and handling the computational demands caused by
their extensive length. Mamba addresses these challenges by overcoming the
local perception limitations of convolutional neural networks and the quadratic
computational complexity of Transformers. Given its advantages over these
mainstream foundation architectures, Mamba exhibits great potential to be a
visual foundation architecture. Since January 2024, Mamba has been actively
applied to diverse computer vision tasks, yielding numerous contributions. To
help keep pace with the rapid advancements, this paper reviews visual Mamba
approaches, analyzing over 200 papers. This paper begins by delineating the
formulation of the original Mamba model. Subsequently, it delves into
representative backbone networks, and applications categorized using different
modalities, including image, video, point cloud, and multi-modal. Particularly,
we identify scanning techniques as critical for adapting Mamba to vision tasks,
and decouple these scanning techniques to clarify their functionality and
enhance their flexibility across various applications. Finally, we discuss the
challenges and future directions, providing insights into new outlooks in this
fast evolving area. A comprehensive list of visual Mamba models reviewed in
this work is available at
https://github.com/Ruixxxx/Awesome-Vision-Mamba-Models.
