VLM-PAR: A Vision Language Model for Pedestrian Attribute Recognition

• URL: http://arxiv.org/abs/2512.22217v1
• Date: Mon, 22 Dec 2025 11:19:04 GMT
• Title: VLM-PAR: A Vision Language Model for Pedestrian Attribute Recognition
• Abstract summary: We introduce VLM-PAR, a modular vision-language framework built on frozen SigLIP 2 multilingual encoders.<n>VLM-PAR achieves significant accuracy improvement on the highly imbalanced PA100K benchmark, setting a new state-of-the-art performance.<n>These results underscore the efficacy of integrating large-scale vision-language pretraining with targeted cross-modal refinement to overcome imbalance and generalization challenges in Pedestrian Attribute Recognition.
• Score: 14.461466154247544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pedestrian Attribute Recognition (PAR) involves predicting fine-grained attributes such as clothing color, gender, and accessories from pedestrian imagery, yet is hindered by severe class imbalance, intricate attribute co-dependencies, and domain shifts. We introduce VLM-PAR, a modular vision-language framework built on frozen SigLIP 2 multilingual encoders. By first aligning image and prompt embeddings via refining visual features through a compact cross-attention fusion, VLM-PAR achieves significant accuracy improvement on the highly imbalanced PA100K benchmark, setting a new state-of-the-art performance, while also delivering significant gains in mean accuracy across PETA and Market-1501 benchmarks. These results underscore the efficacy of integrating large-scale vision-language pretraining with targeted cross-modal refinement to overcome imbalance and generalization challenges in PAR.

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