Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2512.03745v1
• Date: Wed, 03 Dec 2025 12:43:16 GMT
• Title: Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification
• Authors: Jiaze Li, Yan Lu, Bin Liu, Guojun Yin, Mang Ye,
• Abstract summary: We propose a Dual-level Modality Debiasing Learning framework that implements debiasing at both the model and optimization levels.<n>Experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model.
• Score: 59.59359638389348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Two-stage learning pipeline has achieved promising results in unsupervised visible-infrared person re-identification (USL-VI-ReID). It first performs single-modality learning and then operates cross-modality learning to tackle the modality discrepancy. Although promising, this pipeline inevitably introduces modality bias: modality-specific cues learned in the single-modality training naturally propagate into the following cross-modality learning, impairing identity discrimination and generalization. To address this issue, we propose a Dual-level Modality Debiasing Learning (DMDL) framework that implements debiasing at both the model and optimization levels. At the model level, we propose a Causality-inspired Adjustment Intervention (CAI) module that replaces likelihood-based modeling with causal modeling, preventing modality-induced spurious patterns from being introduced, leading to a low-biased model. At the optimization level, a Collaborative Bias-free Training (CBT) strategy is introduced to interrupt the propagation of modality bias across data, labels, and features by integrating modality-specific augmentation, label refinement, and feature alignment. Extensive experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model.

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