AAPAR: CLIP-Based Adaptation and Alignment for Pedestrian Attribute Recognition

Pedestrian Attribute Recognition (PAR) aims to identify attributes of target pedestrians in images. While current methods improve performance through attribute localization and correlation modeling, CLIP-based approaches demonstrate superior effectiveness by leveraging textual semantics to mine attribute relationships. However, two critical limitations persist: (1) existing CLIP adaptation methods in feature extraction networks fail to adequately bridge the gap between CLIP’s design purpose (single-label modality alignment) and PAR’s task nature (multi-label recognition with inter-attribute correlations); and (2) inadequate exploitation of attribute hierarchies. To address these challenges, we propose AAPAR, a CLIP-based adaptation and alignment model with two key contributions: (1) a modality-aligned CLIP adaptation method that integrates Transformer layers as adaptation modules after the image encoder while preserving crucial pre-adaptation image-attribute caption alignment, jointly enhancing multi-label dependencies while preserving original alignment knowledge; and (2) a hierarchical classification architecture comprising global-context, part-aware local, and refined individual attribute classifiers that collectively model attribute relationships at multiple granularities. Extensive experiments on standard PAR datasets (PETA, PA-100K, and RAPv2) and UPAR2024 dataset demonstrate that AAPAR outperforms state-of-the-art methods. The source code will be released on https://github.com/lixusky/AAPAR.