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 $\mathcal{O}(N^2)$. 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
$\mathcal{O}(N)$. 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 $1.3$G FLOPs improves
Vim-Ti with $1.5$G FLOPs by a large margin of $5.6\%$ accuracy on ImageNet.
Code is available at: https://github.com/TerryPei/EfficientVMamba.

基于轻量级模型开发的先前努力主要集中在 CNN 和 Transformer 基础设计上，但仍面临持久挑战。本研究受到 Mamba 等状态空间模型在语言建模和计算机视觉等多个任务中卓越表现和竞争力的启发，探索了视觉状态空间模型在轻量级模型设计中的潜力，并提出了一种新的高效模型变体 EfficientVMamba，通过有效的跳跃采样集成一种基于空洞的选择性扫描方法，构成旨在利用全局和局部表征特征的构件。此外，我们还调查了 SSM 模块与卷积之间的整合，并引入了一种高效的视觉状态空间块，结合了额外的卷积分支，进一步提高了模型的性能。实验结果表明，EfficientVMamba 降低了计算复杂性，同时在各种视觉任务中取得了有竞争力的结果。例如，我们的 EfficientVMamba-S 在 ImageNet 上对比 1.3G FLOPs 的 Vim-Ti 取得了大幅 5.6% 的准确率提升。代码可在以下链接找到: https://github.com/TerryPei/EfficientVMamba。