Abstract
Visible-Infrared Person Re-Identification (VI-ReID) is a challenging cross-modal matching task due to significant modality discrepancies. While current methods mainly focus on learning modality-invariant features through unified embedding spaces, they often focus solely on the common discriminative semantics across modalities while disregarding the critical role of modality-specific identity-aware knowledge in discriminative feature learning. To bridge this gap, we propose a novel Identity Clue Refinement and Enhancement (ICRE) network to mine and utilize the implicit discriminative knowledge inherent in modality-specific attributes. Initially, we design a Multi-Perception Feature Refinement (MPFR) module that aggregates shallow features from shared branches, aiming to capture modality-specific attributes that are easily overlooked. Then, we propose a Semantic Distillation Cascade Enhancement (SDCE) module, which distills identity-aware knowledge from the aggregated shallow features and guide the learning of modality-invariant features. Finally, an Identity Clues Guided (ICG) Loss is proposed to alleviate the modality discrepancies within the enhanced features and promote the learning of a diverse representation space. Extensive experiments across multiple public datasets clearly show that our proposed ICRE outperforms existing SOTA methods.
Abstract (translated)
可见光-红外人物重新识别（VI-ReID）是一项具有挑战性的跨模态匹配任务，由于存在显著的模态差异性。虽然当前的方法主要集中在通过统一嵌入空间学习模态不变特征上，但它们往往只关注各模态之间共同判别语义的同时忽略了特定模态身份感知知识在判别特征学习中的关键作用。为了填补这一空白，我们提出了一种新颖的身份线索精炼与增强（ICRE）网络来挖掘并利用特定模态属性中蕴含的隐性判别知识。 首先，我们设计了一个多感知特征精炼（MPFR）模块，该模块聚合了来自共享分支的浅层特征，旨在捕捉容易被忽视的特定于模态的属性。然后，我们提出了一个语义蒸馏级联增强（SDCE）模块，该模块从聚合的浅层特征中蒸馏出身份感知知识，并指导模态不变特征的学习过程。最后，提出了一种由身份线索引导（ICG）损失函数，以减轻增强后的特征中的模态差异，并促进多样表示空间的学习。 通过多个公共数据集进行的广泛实验清楚地表明，我们提出的ICRE方法优于现有的最先进的方法。
URL
https://arxiv.org/abs/2512.04522
