Mamba用线性复杂度解决视觉模型长距离依赖难题
Mamba in Vision: A Comprehensive Survey of Techniques and Applications
- 用选择性状态空间模型替代注意力机制，实现线性计算
- 在图像分类等任务中表现媲美ViT但速度更快
- 适合追求高效视觉建模的研究者和工程师
Mamba作为一种新兴方法，旨在克服卷积神经网络（CNN）和视觉变压器（ViT）在计算机视觉中的挑战。虽然CNN擅长提取局部特征，但难以捕捉长程依赖关系，需复杂结构改进；而ViT虽能有效建模全局关系，却因自注意力机制的二次计算复杂度导致高开销。Mamba通过选择性结构化状态空间模型，在保持线性计算复杂度的同时，高效捕捉长程依赖。本综述分析了Mamba模型的独特贡献、计算优势及其在视觉任务中的应用，并指出现有挑战与未来研究方向。为推动Mamba在计算机视觉领域的理解与发展提供基础资源。相关概述见：https://github.com/maklachur/Mamba-in-Computer-Vision。
原文摘要 · Abstract (English)
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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