VRCLIP：面向CLIP驱动的视觉-无线电行人重识别的多模态典型相关对齐
VRCLIP: Multimodal Canonical Correlation Alignment for CLIP-Driven Vision-Radio Person Re-Identification
把跨模态对齐从分布匹配改成典型相关分析（CCA）子空间上的相关性最大化，避免过度正则化削弱模态内判别性；这个思路可直接迁移到RGB-红外、RGB-深度、RGB-文本等任意异质模态ReID或检索任务的特征融合环节。
原文依据 we reformulate cross-modal alignment as a correlation maximization problem, avoiding direct constraints on feature distributions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多模态行人重识别（ReID）旨在利用异质模态的互补信息可靠地关联特定个体。相比之下，低频射频（RF）信号凭借其优异的穿透能力和光照不变性，可提供理想的互补信息。然而，融合这些异质模态的关键挑战在于，传统方法严重依赖跨模态分布匹配，这往往会过度正则化并削弱各模态内部的判别能力。典型相关分析（CCA）并不强制进行直接的分布对齐，而是构建一个最大化跨模态相关性的共享子空间，从而在模态特异性和共享语义之间实现内在平衡。受此启发，我们将跨模态对齐重新表述为相关性最大化问题，避免对特征分布施加直接约束，并引导模型协调模态内判别学习与跨模态对齐。具体而言，VRCLIP首先利用光照解耦目标对CLIP的视觉编码器进行精炼，然后在典型相关子空间中实现RGB与RF嵌入的对齐，最后采用以RF为锚点的可靠性门控进行自适应融合。为推动该领域发展，我们将发布VRR，这是首个大规模视觉-无线电ReID数据集，包含超过65万对图像-雷达样本以及31名参与者的位置标注。大量实验表明，该方法取得了93.9%平均精度均值的最优性能，并在多种光照和遮挡条件下展现出稳健的泛化能力。
英文原文摘要 1601 字符
Multimodal person Re-IDentification (ReID) aims to reliably associate specific individuals by utilizing complementary information from heterogeneous modalities. In contrast, low-frequency radio frequency (RF) signals, with their superior penetration capability and illumination invariance, provide ideal complementary information. However, a key challenge in fusing these heterogeneous modalities lies in the conventional approach that relies heavily on cross-modal distribution matching, which often over-regularizes and weakens the discriminative capacity within each modality. Rather than enforcing direct distribution alignment, canonical correlation analysis (CCA) constructs a shared subspace that maximizes cross-modal correlation, inherently balancing modality specificity and shared semantics. Inspired by this, we reformulate cross-modal alignment as a correlation maximization problem, avoiding direct constraints on feature distributions and guiding the model to harmonize intra-modal discriminative learning with cross-modal alignment. Specifically, VRCLIP first refines CLIP's visual encoder with illumination-disentangling objectives, then aligns RGB and RF embeddings in a canonical correlation subspace, and finally employs an RF-anchored reliability gate for adaptive fusion. To advance the area, we will release VRR, the first large-scale vision-radio ReID dataset with over 650K paired image-radar samples and position annotations for 31 participants. Extensive experiments show state-of-the-art 93.9% mAP and robust generalization across diverse lighting and occlusion conditions.