Mamba, a recent selective structured state space model, performs excellently
on long sequence modeling tasks. Mamba mitigates the modeling constraints of
convolutional neural networks and offers advanced modeling capabilities similar
to those of Transformers, through global receptive fields and dynamic
weighting. Crucially, it achieves this without incurring the quadratic
computational complexity typically associated with Transformers. Due to its
advantages over the former two mainstream foundation models, Mamba exhibits
great potential to be a visual foundation model. Researchers are actively
applying Mamba to various computer vision tasks, leading to numerous emerging
works. To help keep pace with the rapid advancements in computer vision, this
paper aims to provide a comprehensive review of visual Mamba approaches. This
paper begins by delineating the formulation of the original Mamba model.
Subsequently, our review of visual Mamba delves into several representative
backbone networks to elucidate the core insights of the visual Mamba. We then
categorize related works using different modalities, including image, video,
point cloud, multi-modal, and others. Specifically, for image applications, we
further organize them into distinct tasks to facilitate a more structured
discussion. Finally, we discuss the challenges and future research directions
for visual Mamba, providing insights for future research in this quickly
evolving area. A comprehensive list of visual Mamba models reviewed in this
work is available at this https URL

在这篇综述性文章中，我们回顾了 Mamba 模型的起源和核心见解，并将 Mamba 应用于不同的计算机视觉任务。我们对各种图像、视频、点云、多模态等应用进行了分类和组织，为未来在这个快速发展的领域中提供了挑战和研究方向。

Vision Mamba: 模型、应用和挑战综述

A Survey on Vision Mamba: Models, Applications and Challenges

This work provides empirical evidence that Mamba, a newly proposed selective
structured state space model, has similar in-context learning (ICL)
capabilities as transformers. We evaluated Mamba on tasks involving simple
function approximation as well as more complex natural language processing
problems. Our results demonstrate that across both categories of tasks, Mamba
matches the performance of transformer models for ICL. Further analysis reveals
that like transformers, Mamba appears to solve ICL problems by incrementally
optimizing its internal representations. Overall, our work suggests that Mamba
can be an efficient alternative to transformers for ICL tasks involving longer
input sequences.

这项研究提供了实证证据，证明了一种新提出的选择性结构化状态空间模型 Mamba 具有类似于 transformers 的上下文学习（ICL）能力。我们在涉及简单函数逼近和更复杂的自然语言处理问题的任务上评估了 Mamba。我们的结果表明，在两类任务中，Mamba 与 transformer 模型在 ICL 方面的性能相当。进一步的分析表明，像 transformer 一样，Mamba 似乎通过逐步优化其内部表示来解决 ICL 问题。总体而言，我们的研究表明，对于涉及更长输入序列的 ICL 任务，Mamba 可以作为 transformers 的高效替代品。