VMambaCC: A Visual State Space Model for Crowd Counting

TL;DR

This paper introduces VMambaCC, a crowd counting model that combines a low-cost, globally receptive visual model with novel attention and feature pyramid mechanisms, achieving high accuracy on public datasets.

Contribution

The paper proposes VMambaCC, integrating a new attention mechanism and feature pyramid network to improve crowd counting accuracy with low computational cost.

Findings

Achieves a MAE of 51.87 on ShangHaiTech_PartA dataset.

Demonstrates effectiveness across five public datasets.

Enhances spatial feature representation with novel attention mechanisms.

Abstract

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.