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    State space models (SSMs) efficiently capture long-term dependencies in data. Mamba, a new architecture, extends SSMs for visual tasks, potentially surpassing Transformers.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Deep Learning

    Background:

    • State space models (SSMs) are mathematical tools for dynamic systems analysis.
    • SSMs excel in sequence data processing, capturing long-term dependencies with linear complexity.
    • Modern SSMs, like Mamba, integrate time-varying parameters for efficient training and inference.

    Purpose of the Study:

    • To survey and taxonomize the applications of Mamba in the visual domain.
    • To analyze Mamba's potential to surpass existing architectures like Transformers.
    • To provide a comprehensive overview of Mamba's predecessors, recent advances, and impact.

    Main Methods:

    • Comprehensive literature review of Mamba-based research in visual domains.
    • Taxonomy development categorizing Mamba's applications by visual task and data type.
    • Analysis of Mamba's architectural innovations and performance characteristics.

    Main Results:

    • Mamba demonstrates strong capabilities in various visual tasks, including general vision, multimodal learning, medical image analysis, and remote sensing.
    • The architecture maintains efficiency and effective long-range dependency modeling in visual data.
    • Mamba shows promise as a successor to Transformer architectures in visual applications.

    Conclusions:

    • Mamba represents a significant advancement in deep learning for visual data processing.
    • Its efficient architecture and strong performance suggest a transformative impact on computer vision.
    • Further research into Mamba's extensions and applications across diverse visual domains is warranted.