Visual Mamba

Visual Mamba, a novel architecture based on state space models, aims to improve upon the limitations of convolutional neural networks (CNNs) and transformers in various computer vision tasks. Current research focuses on adapting Mamba's efficient long-range dependency modeling capabilities through different scanning techniques and integrating it with existing architectures like UNets and transformers for applications such as image segmentation, remote sensing, and physiological signal processing. This approach offers the potential for improved accuracy and efficiency in computationally intensive vision tasks, impacting fields ranging from medical image analysis to infrastructure monitoring.