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Mamba in Vision: A Comprehensive Survey of Techniques and Applications
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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.
Forward citations
Cited by 8 Pith papers
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Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants
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Prompt-S6 plus semantic token pruning and progressive tri-modal fusion improves multi-spectral object ReID accuracy and efficiency on four benchmarks.
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ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
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MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
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HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction
HiFi-Mamba uses stacked W-Laplacian spectral decoupling and unidirectional HiFi-Mamba blocks to improve high-frequency detail preservation and efficiency over prior Mamba, CNN, and Transformer models for MRI reconstruction.
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A multi-task spatiotemporal deep neural network for predicting penetration depth and morphology in laser welding
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State Space Models Meet Remote Sensing: A Survey
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
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