VMambaCC: A Visual State Space Model for Crowd Counting
This work addresses crowd counting, a domain-specific computer vision task, with incremental improvements in accuracy and efficiency.
The authors tackled crowd counting by proposing VMambaCC, a visual state space model that incorporates a novel Multi-head High-level Feature attention mechanism and a High-level Semantic Supervised Feature Pyramid Network, achieving a mean absolute error of 51.87 and mean squared error of 81.3 on the ShangHaiTech_PartA dataset.
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.