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Mamba in Vision: A Comprehensive Survey of Techniques and Applications

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arxiv 2410.03105 v1 pith:75AY3ATT submitted 2024-10-04 cs.CV cs.AIcs.CLcs.LG

Md Maklachur Rahman , Abdullah Aman Tutul , Ankur Nath , Lamyanba Laishram , Soon Ki Jung , Tracy Hammond This is my paper · ORCID
classification cs.CVcs.AIcs.CLcs.LG
keywords mambavisioncomputationalmodelsapplicationscapturechallengescnns
verification ladder T0 review T1 audit T2 compute T3 formal
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Mamba is emerging as a novel approach to overcome the challenges faced by Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision. While CNNs excel at extracting local features, they often struggle to capture long-range dependencies without complex architectural modifications. In contrast, ViTs effectively model global relationships but suffer from high computational costs due to the quadratic complexity of their self-attention mechanisms. Mamba addresses these limitations by leveraging Selective Structured State Space Models to effectively capture long-range dependencies with linear computational complexity. This survey analyzes the unique contributions, computational benefits, and applications of Mamba models while also identifying challenges and potential future research directions. We provide a foundational resource for advancing the understanding and growth of Mamba models in computer vision. An overview of this work is available at https://github.com/maklachur/Mamba-in-Computer-Vision.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 11 citations worldwide. Full citation record

  1. Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants

    cs.CY 2025-08 conditional novelty 7.0 of 10

    FlowState, an 18.6M-parameter SSM with a functional-basis decoder, reaches top GIFT-Eval MASE and CRPS and generalizes to sampling rates not seen in training.

  2. Multi-Modal Object Re-Identification with Prompt-S6 and Semantic-Aware Knowledge Guidance

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Prompt-S6 plus semantic token pruning and progressive tri-modal fusion improves multi-spectral object ReID accuracy and efficiency on four benchmarks.

  3. DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction

    eess.IV 2025-01 conditional novelty 6.0 of 10

    DH-Mamba is a dual-domain hierarchical Mamba network that uses circular k-space scanning and local diversity enhancement to outperform prior MRI reconstruction methods on three public datasets.

  4. MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation

    cs.CV 2024-12 conditional novelty 4.0 of 10

    MambaU-Lite, a 0.42M-parameter hybrid Mamba-CNN model, reports DSC/IoU of 0.9057/0.8361 on ISIC2018 and 0.9572/0.9189 on PH2, best among the compared lightweight models.

  5. Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning

    cs.LG 2024-12 conditional novelty 2.0 of 10

    A survey of Graph Mamba, the adaptation of state-space models (Mamba, S4, S6) to graph learning, synthesizing roughly 30 recent papers into a taxonomy of architectures, applications, benchmarks, and open challenges.

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