USIL: Uncertainty-Aware Selective Invariant Learning for Low-Light Visible-Infrared Person Re-Identification

Low-light visible–infrared person re-identification (VI-ReID) aims to retrieve the same pedestrian across heterogeneous cameras under severe illumination degradation. The coexistence of reliable identity structures and uncertain cross-spectral observations can contaminate invariant representations when no cue-wise distinction is made. To address this problem, we propose a novel Uncertainty-Aware Selective Invariant Learning (USIL) framework. USIL consists of an Uncertainty-Aware Selective Invariant Modeling mechanism that selectively propagates stable identity cues and estimates location-wise uncertainty, and an Uncertainty-Guided Low-Light Enhancement strategy that uses the resulting reliability to regulate feature compensation and descriptor aggregation. Furthermore, Distributional Cross-modal Retrieval-Risk Regularization is introduced to constrain the upper tail of batch-wise ranking violations through Bayesian-quadrature-inspired Dirichlet reweighting. Extensive experiments on three standard VI-ReID benchmarks demonstrate consistent improvements over the reproduced IRL baseline, including gains of 0.77% Rank-1 and 1.10% mAP on SYSU-MM01 All-Search.