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EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba

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arxiv 2403.09977 v1 pith:F3P3POZL submitted 2024-03-15 cs.CV cs.AI

Xiaohuan Pei , Tao Huang , Chang Xu This is my paper · ORCID
classification cs.CVcs.AI
keywords efficientvmambamodelefficientglobalspacestatevisualwhile
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abstract

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: \url{https://github.com/TerryPei/EfficientVMamba}.

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Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection

    cs.CV 2025-08 conditional novelty 6.0 of 10

    SNARM combines memory-bank residuals, self-referential in-image residuals, and residual-guided Mamba scanning to report state-of-the-art anomaly detection scores on three benchmarks.

  2. Training-free Token Reduction for Vision Mamba

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MTR uses Mamba's timescale parameter Δ as a token importance score to merge unimportant tokens, giving training-free inference speedups with small accuracy loss.

  3. LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models

    eess.IV 2025-07 conditional novelty 6.0 of 10

    A language-driven Mamba framework for low-dose CT denoising uses a frozen vision-language model to provide semantic supervision, achieving marginal but consistent quantitative gains over previous methods.

  4. ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification

    cs.CV 2025-06 conditional novelty 6.0 of 10

    ECP-Mamba, a Mamba-based network with a spiral scan and multi-scale self-distillation, reports state-of-the-art PolSAR image classification accuracy at label rates as low as 0.2%.

  5. DIME-Net: A Dual-Illumination Adaptive Enhancement Network Based on Retinex and Mixture-of-Experts

    cs.CV 2025-08 conditional novelty 5.0 of 10

    A single Retinex-based network with sparse mixture-of-experts tone curves, trained on a mixed low-light/backlit dataset, improves PSNR/SSIM/LPIPS on LOLv1 and BAID without dataset-specific retraining.

  6. HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A state space model based point cloud network with shuffled Hilbert serialization, a convolutional bidirectional S6 branch, and multi-head S6 achieves new top scores on ModelNet40, ShapeNet, S3DIS, and ScanObjectNN.

  7. QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models

    cs.CV 2025-07 conditional novelty 5.0 of 10

    QuarterMap prunes spatial activations before VMamba's four-directional scan and upsamples after, yielding up to 1.11x throughput with under 1% accuracy loss on ImageNet classification.

  8. HybridTM: Combining Transformer and Mamba for 3D Semantic Segmentation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A new 3D semantic segmentation architecture that interleaves attention and Mamba operators within each layer achieves small but consistent gains on indoor and outdoor benchmarks.

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