Crowd counting in domain generalization based on multi-scale attention and hierarchy level enhancement

Abstract

AbstractIn order to solve the problem of weak single domain generalization ability in existing crowd counting methods, this study proposes a new crowd counting framework called Multi-scale Attention and Hierarchy level Enhancement (MAHE). Firstly, the model can focus on both the detailed features and the macro information of structural position changes through the fusion of channel attention and spatial attention. Secondly, the addition of multi-head attention feature module facilitates the model’s capacity to effectively capture complex dependency relationships between sequence elements. In addition, the three-stage encoding and decoding processing mode enables the model to effectively represent crowd density information. Finally, the fusion of multi-scale features derived from different receptive fields is further enhanced through multi-scale hierarchy level feature fusion, thereby enabling the model to learn high-level semantic information and low-level multi-scale visual field feature information. This method enhances the model’s capacity to capture key feature information, even in highly differentiated datasets, thereby improving the model’s generalization ability on a single domain. The model has demonstrated strong generalization capabilities through extensive experiments on different datasets. This study not only improves the accuracy of crowd counting, but also introduces a new research approach for single domain generalization of crowd counting.