As a deep learning model, Visual Mamba (VMamba) has a low computational
complexity and a global receptive field, which has been successful applied to
image classification and detection. To extend its applications, we apply VMamba
to crowd counting and propose a novel VMambaCC (VMamba Crowd Counting) model.
Naturally, VMambaCC inherits the merits of VMamba, or global modeling for
images and low computational cost. Additionally, we design a Multi-head
High-level Feature (MHF) attention mechanism for VMambaCC. MHF is a new
attention mechanism that leverages high-level semantic features to augment
low-level semantic features, thereby enhancing spatial feature representation
with greater precision. Building upon MHF, we further present a High-level
Semantic Supervised Feature Pyramid Network (HS2PFN) that progressively
integrates and enhances high-level semantic information with low-level semantic
information. Extensive experimental results on five public datasets validate
the efficacy of our approach. For example, our method achieves a mean absolute
error of 51.87 and a mean squared error of 81.3 on the ShangHaiTech\_PartA
dataset. Our code is coming soon.

Visual Mamba（VMamba）是一种深度学习模型，具有较低的计算复杂性和全局感受野，成功应用于图像分类和检测。为了扩展其应用领域，我们将 VMamba 应用于人群计数，并提出了一种新颖的 VMambaCC（VMamba 人群计数）模型。

VMambaCC：一种用于人群计数的可视化状态空间模型

VMambaCC: A Visual State Space Model for Crowd Counting

Medical image segmentation is increasingly reliant on deep learning
techniques, yet the promising performance often come with high annotation
costs. This paper introduces Weak-Mamba-UNet, an innovative weakly-supervised
learning (WSL) framework that leverages the capabilities of Convolutional
Neural Network (CNN), Vision Transformer (ViT), and the cutting-edge Visual
Mamba (VMamba) architecture for medical image segmentation, especially when
dealing with scribble-based annotations. The proposed WSL strategy incorporates
three distinct architecture but same symmetrical encoder-decoder networks: a
CNN-based UNet for detailed local feature extraction, a Swin Transformer-based
SwinUNet for comprehensive global context understanding, and a VMamba-based
Mamba-UNet for efficient long-range dependency modeling. The key concept of
this framework is a collaborative and cross-supervisory mechanism that employs
pseudo labels to facilitate iterative learning and refinement across the
networks. The effectiveness of Weak-Mamba-UNet is validated on a publicly
available MRI cardiac segmentation dataset with processed scribble annotations,
where it surpasses the performance of a similar WSL framework utilizing only
UNet or SwinUNet. This highlights its potential in scenarios with sparse or
imprecise annotations. The source code is made publicly accessible.

介绍了一种创新的弱监督学习框架，利用卷积神经网络（CNN）、视觉 Transformer（ViT）和最新的 Visual Mamba（VMamba）架构进行医学图像分割，特别是处理基于涂鸦注释。