Vision Mamba: Efficient Visual Representation Learning with
  Bidirectional State Space Model
Authors: name: Lianghui Zhu, name: Bencheng Liao, name: Qian Zhang, name: Xinlong Wang, name: Wenyu Liu, name: Xinggang Wang

Recently the state space models (SSMs) with efficient hardware-aware designs,
i.e., Mamba, have shown great potential for long sequence modeling. Building
efficient and generic vision backbones purely upon SSMs is an appealing
direction. However, representing visual data is challenging for SSMs due to the
position-sensitivity of visual data and the requirement of global context for
visual understanding. In this paper, we show that the reliance of visual
representation learning on self-attention is not necessary and propose a new
generic vision backbone with bidirectional Mamba blocks (Vim), which marks the
image sequences with position embeddings and compresses the visual
representation with bidirectional state space models. On ImageNet
classification, COCO object detection, and ADE20k semantic segmentation tasks,
Vim achieves higher performance compared to well-established vision
transformers like DeiT, while also demonstrating significantly improved
computation & memory efficiency. For example, Vim is 2.8times faster than
DeiT and saves 86.8% GPU memory when performing batch inference to extract
features on images with a resolution of 1248times1248. The results
demonstrate that Vim is capable of overcoming the computation & memory
constraints on performing Transformer-style understanding for high-resolution
images and it has great potential to become the next-generation backbone for
vision foundation models. Code is available at https://github.com/hustvl/Vim.