title: EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
summary: Prior efforts in light-weight model development mainly centered on CNN and
  Transformer-based designs yet faced persistent challenges. CNNs adept at local
  feature extraction compromise resolution while Transformers offer global reach
  but escalate computational demands O(N^2). This ongoing trade-off
  between accuracy and efficiency remains a significant hurdle. Recently, state
  space models (SSMs), such as Mamba, have shown outstanding performance and
  competitiveness in various tasks such as language modeling and computer vision,
  while reducing the time complexity of global information extraction to
  O(N). Inspired by this, this work proposes to explore the potential
  of visual state space models in light-weight model design and introduce a novel
  efficient model variant dubbed EfficientVMamba. Concretely, our EfficientVMamba
  integrates a atrous-based selective scan approach by efficient skip sampling,
  constituting building blocks designed to harness both global and local
  representational features. Additionally, we investigate the integration between
  SSM blocks and convolutions, and introduce an efficient visual state space
  block combined with an additional convolution branch, which further elevate the
  model performance. Experimental results show that, EfficientVMamba scales down
  the computational complexity while yields competitive results across a variety
  of vision tasks. For example, our EfficientVMamba-S with 1.3G FLOPs improves
  Vim-Ti with 1.5G FLOPs by a large margin of 5.6% accuracy on ImageNet.
  Code is available at: https://github.com/TerryPei/EfficientVMamba.
paper_authors._id: 65f7ad0f933dd81304b79552
paper_authors.name: Xiaohuan Pei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T09:07:34.813Z
paper_authors._id: 6318661051edb569f1b4e68b
paper_authors.avatarUrl: /avatars/9b98943bc609ca974abf0abff3e68633.svg
paper_authors.fullname: TerryPei
paper_authors.type: user
paper_authors.user: TerryPei
paper_authors._id: 65f7ad0f933dd81304b79553
paper_authors.name: Tao Huang
paper_authors._id: 65f7ad0f933dd81304b79554
paper_authors.name: Chang Xu
paper_title: EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
paper_summary: Prior efforts in light-weight model development mainly centered on CNN and
  Transformer-based designs yet faced persistent challenges. CNNs adept at local
  feature extraction compromise resolution while Transformers offer global reach
  but escalate computational demands O(N^2). This ongoing trade-off
  between accuracy and efficiency remains a significant hurdle. Recently, state
  space models (SSMs), such as Mamba, have shown outstanding performance and
  competitiveness in various tasks such as language modeling and computer vision,
  while reducing the time complexity of global information extraction to
  O(N). Inspired by this, this work proposes to explore the potential
  of visual state space models in light-weight model design and introduce a novel
  efficient model variant dubbed EfficientVMamba. Concretely, our EfficientVMamba
  integrates a atrous-based selective scan approach by efficient skip sampling,
  constituting building blocks designed to harness both global and local
  representational features. Additionally, we investigate the integration between
  SSM blocks and convolutions, and introduce an efficient visual state space
  block combined with an additional convolution branch, which further elevate the
  model performance. Experimental results show that, EfficientVMamba scales down
  the computational complexity while yields competitive results across a variety
  of vision tasks. For example, our EfficientVMamba-S with 1.3G FLOPs improves
  Vim-Ti with 1.5G FLOPs by a large margin of 5.6% accuracy on ImageNet.
  Code is available at: https://github.com/TerryPei/EfficientVMamba.
paper_ai_summary: The proposed EfficientVMamba combines visual state space models with convolutions to reduce computational complexity while maintaining competitive performance in vision tasks.
paper_ai_keywords: state space models SSM Mamba EfficientVMamba atrous-based selective scan skip sampling global representational features local representational features Efficient Visual State Space block convolution branch ImageNet FLOPs