Work

Rethinking attention mechanism for enhanced pedestrian attribute recognition

Year: 2025

Type: article

Abstract: Pedestrian Attribute Recognition (PAR) plays a crucial role in various computer vision applications, demanding precise and reliable identification of attributes from pedestrian images. Traditional PAR methods, though effective in leveraging attention mechanisms, often suffer from the lack of direct supervision on attention, leading to potential overfitting and misallocation. This paper introduces a novel and model-agnostic approach, Attention-Aware Regularization (AAR), which rethinks the attention mechanism by integrating causal reasoning to provide direct supervision of attention maps. AAR employs perturbation techniques and a unique optimization objective to assess and refine attention quality, encouraging the model to prioritize attribute-specific regions. Our method demonstrates significant improvement in PAR performance by mitigating the effects of incorrect attention and fostering a more effective attention mechanism. Experiments on standard datasets showcase the superiority of our approach over existing methods, setting a new benchmark for attention-driven PAR models.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.130236

Authors: Junyi Wu, Yan Huang, Min Gao, Yuzhen Niu, Yuzhong Chen, Qiang Wu

Institutions: Fuzhou University, University of Technology Sydney

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,670

Funders: National Natural Science Foundation of China, Natural Science Foundation of Fujian Province