A dual-aligned knowledge self-distillation framework for visible-infrared cross-modal person re-identification
Siyuan Deng, Kunhao Yuan, Gerald Schaefer, Shihua Zhou, George Vogiatzis, Yifan Wang, Hui Fang · Knowledge-Based Systems · 2025
• Dual alignment knowledge self-distillation to better capture modality-invariant/specific features for VI-ReID • Temperature-modulated alignment and confidence-based selective masking to enhance model reliability. • CutSwap augmentation to improve model robustness against intra-class variations and modality discrepancies. • State-of-the-art performance on SYSU-MM01 and RegDB benchmarks. Visible-infrared person re-identification (VI-ReID) significantly enhances identity retrieval across different illumination conditions by matching visible and infrared modalities. However, existing contrastive-learning-based approaches predominantly focus on cross-modal feature alignment, thus undermining model reliability in complex scenarios. To address this challenge, we introduce a Dual Alignment Knowledge Distillation (DAKD) framework that leverages comprehensive self-distillation at both instance and class levels. Our framework incorporates a temperature-modulated alignment strategy, capturing rich modality-invariant generalities as well as modality-specific discriminative details. Additionally, we propose a confidence-based selective masking mechanism that guides the distillation towards confident and informative teacher predictions. To further enhance robustness against modality discrepancies and intra-class variations, we develop a dedicated augmentation technique, CutSwap, which exchanges image channels to simulate realistic cross-modality variations. Extensive experiments on the benchmark SYSU-MM01 and RegDB datasets demonstrate superior performance compared to other state-of-the-art methods, achieving rank-1 accuracies of 76.31% and 94.83%, respectively and validating the efficacy of DAKD in maintaining robust cross-modal alignment while preserving essential identity-specific discriminative information.