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Vision Mamba: A Comprehensive Survey and Taxonomy

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arxiv 2405.04404 v1 pith:55RAPIDZ submitted 2024-05-07 cs.CV cs.AIcs.CLcs.LG

Xiao Liu , Chenxu Zhang , Lei Zhang This is my paper
classification cs.CVcs.AIcs.CLcs.LG
keywords mambadatasurveyanalysisdomainmodelsequencespace
verification ladder T0 review T1 audit T2 compute T3 formal
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State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy.

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Cited by 8 Pith papers

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  1. Bengal-HP_RU: A Dataset of Bengal People For Head Pose Estimation

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    Bengal-HP_RU is the first publicly available head pose dataset for Bengali subjects, with 12,894 images collected from Wikimedia Commons and partitioned by uploader identity.

  2. ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    ABMamba uses Mamba-based linear-complexity processing plus a novel Aligned Hierarchical Bidirectional Scan to deliver competitive video captioning on VATEX and MSR-VTT at roughly 3x higher throughput than typical Tran...

  3. RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet

    eess.SP 2025-07 conditional novelty 6.0 of 10

    On the RadioMapSeer benchmark, RadioMamba reports NMSE 0.0050 versus 0.0072 for RadioDiff, with 28 ms inference and 8.6M parameters.

  4. 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.

  5. StampFormer: A Physics-Guided Material-Geometry-Coupled Multimodal Model for Rapid Prediction of Physical Fields in Sheet Metal Stamping

    cs.LG 2026-05 unverdicted novelty 5.0 of 10

    StampFormer fuses geometry and material properties in a Swin-UNet backbone with custom modules to predict stamping FEA fields at <8.5% relative error in under one second.

  6. Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation

    cs.CV 2025-08 conditional novelty 5.0 of 10

    PDSSNet reports state-of-the-art mIoU of 84.68, 87.55, and 56.10 on the Vaihingen, Potsdam, and LoveDA remote sensing datasets by combining GT-derived prototypes, a Mamba-style semantic-structure module, and a similar...

  7. VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation

    cs.CV 2025-09 conditional novelty 4.0 of 10

    VCMamba reports that using convolutional feed-forward blocks for the first three stages followed by multi-directional Mamba blocks in the final stage yields 82.6% ImageNet-1K and 47.1 ADE20K mIoU at 31.5M parameters, ...

  8. A Survey of Mamba

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    The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.

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