ALFormer: Attribute localization transformer in pedestrian attribute recognition

Abstract

Pedestrian attribute recognition is an important task for intelligent video surveillance. However, existing methods struggle to accurately localize discriminative regions for each attribute. We propose Attribute Localization Transformer (ALFormer), a novel framework to improve spatial localization through two key components. First, we introduce Mask Contrast Learning (MCL) to suppress regional feature relevance, forcing the model to focus on intrinsic spatial areas for each attribute. Second, we design an Attribute Spatial Memory (ASM) module to generate reliable attention maps that capture inherent locations for each attribute. Extensive experiments on two benchmark datasets demonstrate state-of-the-art performance of ALFormer. Ablation studies and visualizations verify the effectiveness of the proposed modules in improving attribute localization. Our work provides a simple yet effective approach to exploit spatial consistency for enhanced pedestrian attribute recognition.