  Designing computationally efficient network architectures persists as an
ongoing necessity in computer vision. In this paper, we transplant Mamba, a
state-space language model, into VMamba, a vision backbone that works in linear
time complexity. At the core of VMamba lies a stack of Visual State-Space (VSS)
blocks with the 2D Selective Scan (SS2D) module. By traversing along four
scanning routes, SS2D helps bridge the gap between the ordered nature of 1D
selective scan and the non-sequential structure of 2D vision data, which
facilitates the gathering of contextual information from various sources and
perspectives. Based on the VSS blocks, we develop a family of VMamba
architectures and accelerate them through a succession of architectural and
implementation enhancements. Extensive experiments showcase VMamba's promising
performance across diverse visual perception tasks, highlighting its advantages
in input scaling efficiency compared to existing benchmark models. Source code
is available at https://github.com/MzeroMiko/VMamba.
