FreqMamba: Frequency Enhancement and Mamba-Driven Representation Learning for Pedestrian Attribute Recognition

Abstract

This paper focuses on research into multimodal pedestrian attribute recognition (PAR). Existing multimodal PAR methods face three major challenges: 1) reliance on fixed prompt templates that limit refined textual attribute descriptions, 2) neglect of essential visual details, such as textures and contours, which are crucial for distinguishing subtle attributes, and 3) insufficient interaction between visual and textual modalities, resulting in suboptimal recognition performance. To address these challenges, we propose a Frequency Enhancement and Mamba-Driven Representation Learning (FreqMamba) framework. Specifically, we design a Dynamic Prompt Strategy (DPS) that dynamically adapts textual descriptions based on visual features, resulting in refined textual descriptions. Additionally, a Frequency-Enhanced Module (FEM) is introduced to capture texture and contour details in the frequency domain, enriching visual features and further enhancing the generation of dynamic prompts. Finally, we propose a Mamba-Driven Modality Interaction (MDMI) method combined with a Bidirectional Modality Contrastive Loss (BMCLoss) to facilitate effective interaction between modalities, integrating visual and textual information comprehensively. Extensive experiments on multiple PAR datasets, including PETA, PA100K, RAPv1, and RAPv2, demonstrate the superiority of our FreqMamba approach over existing methods.