状态空间模型（State Space Models，简称SSM）在控制理论中传统用于通过状态变量对动态系统建模。Aaron R....
为了解决上面的问题,作者提出了一种新的选择性 SSM(Selective State Space Models,简称 S6 或 Mamba)。这种模型通过让 SSM 的矩阵 A、B、C 依赖于输入数据,从而实现了选择性。这意味着模型可以根据当前的输入动态地调整其状态,选择性地传播或忽略信息。 Mamba 集成了 S4 和 Transformer 的精华,一个更加高效(S4)...
将S6 与 CSM 集成,称为 S6 块,作为构建视觉状态空间(Visual State Space,VSS)块的核心元素,构成了 VMamba 的基本构建块。S6 块继承了选择性扫描机制的线性复杂性,同时保留了全局感受野。 VMamba 整体架构:VMamba-Tiny 的架构如下图所示。首先使用一个Stem节点将输入图像分割成多个patchs,类似于 ViTs,但没有将...
xmindflow/Awesome_Mamba Star228 Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis natural-language-processingcomputer-visiondeep-learningtime-seriessurveymedical-imagingremote-sensingspeech-processingmambamedical-image-processingimage-enhancementmedical-image-analysis...
基础模型(Foundation models,FM),即在海量数据上进行预训练,然后针对下游任务进行调整的大型模型。 这些基础模型的骨干通常是序列模型,可在语言、图像、语音、音频、时间序列和基因组学等各种领域的任意输入序列上运行。 现代FM 主要基于一种单一类型的序列模型:Transformer及其核心注意力层。
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges The Mamba-360 framework is a collection of State Space Models in various Domains. Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence...
Therefore, in this work, we propose VL-Mamba, a multimodal large language model based on state space models, which have been shown to have great potential for long-sequence modeling with fast inference and linear scaling in sequence length. Specifically, we first replace the transformer-based ...
Exploring Graph Mamba: A Comprehensive Survey on StateSpace Models for Graph LearningarXiv:2412. 18322v1 cs.LG 24 Dec 20
Recently, State-Space Models (SSMs), exemplified by Mamba, have gained significant attention as a promising alternative due to their linear computational complexity. Another approach, neural memory Ordinary Differential Equations (nmODEs), exhibits similar principles and achieves good results. In this ...
Designing computationally efficient network architectures remains an ongoing necessity in computer vision. In this paper, we adapt Mamba, a state-space language model, into VMamba, a vision backbone with linear time complexity. At the core of VMamba is a stack of Visual State-Space (VSS) blocks...