第一个基于 Mamba 的生产级模型，采用新颖的 SSM-Transformer 混合架构；与 Mixtral 8x7B 相比，长上下文上的吞吐量提高了 3 倍；提供对 256K 上下文窗口的访问；公开了模型权重；同等参数规模中唯一能够在单个 GPU 上容纳高达 140K 上下文的模型。模型架构 如下图所示，Jamba 的架构采用块层（blocks-and-layers...
在这项工作中,来自美团的研究团队提出了一种采用 Mamba-Attention 架构、用于视频生成的潜在扩散模型——Matten。Matten 采用空间-时间注意力进行局部视频内容建模,采用双向 Mamba 进行全局视频内容建模,计算成本低。 综合实验评估表明,在基准性能方面,Matten 与当前基于 Transformer 和 GAN 的模型相比具有很强的竞争力,...
通过探索高效的Mamba和表现欠佳的线性注意力Transformer之间的相似性和差异,我们提供了全面的分析,揭示了Mamba成功背后的关键因素。具体来说,我们在统一的公式下重新定义了选择性状态空间模型和线性注意力,将Mamba重新表述为具有六个主要区别的线性注意力Transformer的变体:输入门、遗忘门、快捷连接、无注意力归一化、单头...
Demystify Mamba in Vision: A Linear Attention Perspective 2.1 出发点 在探索Mamba与线性注意力Transformer关系时发现,Mamba的特殊设计中遗忘门和块设计对性能提升贡献大。MLLA模块旨在将这两个关键设计融入线性注意力,以提升其在视觉任务中的性能,同时保持并行计算和快速推理优势。 2.2 原理 2.2.1 选择性状态空间模...
MCI Net: Mamba- Convolutional lightweight self-attention medical image segmentation networkdoi:10.1088/2057-1976/ad8acbWith the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, the most common network models in ...
To address these restrictions, we introduce a dual triple attention module designed to encourage selective modeling of image features, thereby enhancing the overall performance of the segmentation process. We design the feature extraction module based on CNN and VMamba for both local and global ...
They are then processed using Text Attention Mamba (TAM) and Point Clouds Mamba (PCM) for data enhancement and alignment. In the subsequent fine localization stage, the features of the text description and 3D point cloud are cross modally fused and further enhanced through cascaded Cross Attention...
Mar 19, 2025 352a9d1·Mar 19, 2025 History 4 Commits README.md Update README.md Mar 19, 2025 README This repository contains the official implementation ofTrajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM. Trajectory Mamba is an efficient motion forecasting fram...
However, the quadratic complexity of standard self-attention makes Transformers computationally prohibitive for high-resolution images. To address these challenges, we propose MLLA-UNet (Mamba-Like Linear Attention UNet), a novel architecture that achieves linear computational complexity while maintaining ...
for lesion segmentation of breast cancer. Our network consists of a lightweight CNN backbone and a Multi-view Inter-Slice Self-Attention Mamba (MISM) module. The MISM module integrates Visual State Space Block (VSSB) and Inter-Slice Self-Attention (ISSA) mechanism, effectively reducing parameters...