时序原型与层次对齐用于无监督的基于视频的可见-红外人员重识别
Temporal Prototyping and Hierarchical Alignment for Unsupervised Video-based Visible-Infrared Person Re-Identification
AI总结：
本文提出HiTPro框架，通过时序感知特征编码器和层次交叉原型对齐，解决无监督视频基于可见-红外人员重识别问题，实现跨模态一致性与不变性。
摘要：
可见-红外人员重识别（VI-ReID）实现了全天候监控的跨模态身份匹配，但现有方法大多集中在图像层面或依赖昂贵的身份标注。虽然基于视频的VI-ReID最近出现以利用时间动态性提高鲁棒性，但现有研究仍局限于监督设置。关键的是，无监督视频VI-ReID问题，即模型必须从RGB和红外轨迹片段中学习而不使用身份标签，尽管在实际部署中具有重要性，仍鲜有研究。为此，我们提出HiTPro（层次时间原型），一个无需显式硬伪标签分配的原型驱动框架。HiTPro首先使用高效的时序感知特征编码器提取判别帧级特征，并将其聚合为稳健的轨迹级表示。基于这些特征，HiTPro首先通过Intra-Camera Tracklet Prototyping构建可靠的同一摄像头原型，通过聚合时间分割的子轨迹片段的特征。通过层次交叉原型对齐，我们执行一个两阶段的正例挖掘过程：从同一模态关联到跨模态匹配，增强动态阈值策略和软权重分配。最后，{层次对比学习}逐步优化特征-原型对齐，跨三个层面：同一摄像头辨别、跨摄像头同一模态一致性、跨模态不变性。在HITSZ-VCM和BUPTCampus上的大量实验表明，HiTPro在完全无监督设置下实现了最先进的性能，显著优于适配基线，并为未来研究建立了坚实的基准。
英文摘要：
Visible-infrared person re-identification (VI-ReID) enables cross-modality identity matching for all-day surveillance, yet existing methods predominantly focus on the image level or rely heavily on costly identity annotations. While video-based VI-ReID has recently emerged to exploit temporal dynamics for improved robustness, existing studies remain limited to supervised settings. Crucially, the unsupervised video VI-ReID problem, where models must learn from RGB and infrared tracklets without identity labels, remains largely unexplored despite its practical importance in real-world deployment. To bridge this gap, we propose HiTPro (Hierarchical Temporal Prototyping), a prototype-driven framework without explicit hard pseudo-label assignment for unsupervised video-based VI-ReID. HiTPro begins with an efficient Temporal-aware Feature Encoder that first extracts discriminative frame-level features and then aggregates them into a robust tracklet-level representation. Building upon these features, HiTPro first constructs reliable intra-camera prototypes via Intra-Camera Tracklet Prototyping by aggregating features from temporally partitioned sub-tracklets. Through Hierarchical Cross-Prototype Alignment, we perform a two-stage positive mining process: progressing from within-modality associations to cross-modality matching, enhanced by Dynamic Threshold Strategy and Soft Weight Assignment. Finally, {Hierarchical Contrastive Learning} progressively optimizes feature-prototype alignment across three levels: intra-camera discrimination, cross-camera same-modality consistency, and cross-modality invariance. Extensive experiments on HITSZ-VCM and BUPTCampus demonstrate that HiTPro achieves state-of-the-art performance under fully unsupervised settings, significantly outperforming adapted baselines and establishes a strong baseline for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
