发表机构
Dalian University of Technology（大连理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有RE-VReID方法时空建模解耦、多模态融合不足的问题，提出含MAB、PST、HMF模块的Paths框架，在三个基准上验证了有效性。
AI 中文摘要
RGB-Event视频行人重识别（RE-VReID）旨在利用互补的RGB视频与事件流，在非重叠摄像头间检索特定行人。然而现有方法常将空间与时间建模解耦，限制了二者交互；且全局级RGB-事件融合无法充分挖掘细粒度判别线索。为解决这些问题，本文提出Paths，这是一个具备时空建模与分层多模态融合的RE-VReID统一框架。具体而言，首先设计记忆增强骨干网络（MAB）以维护模态特定的身份原型，实现稳定的模态内表示学习；接着提出感知提示的时空Transformer（PST），在统一Transformer内联合建模空间与时间线索；最后引入分层多模态融合（HMF），在全局与局部层面融合RGB与事件特征。借助这些模块，该框架可学习用于RE-VReID的鲁棒且具判别性的表示。在EvReID、MARS、iLIDS-VID三个公开RE-VReID基准上开展的大量实验，验证了所提方法的有效性，代码可在指定URL获取。
英文摘要
RGB-Event Video Person Re-Identification (RE-VReID) aims to retrieve specific person across non-overlapping cameras with complementary RGB videos and event streams. However, existing methods often decouple spatial and temporal modeling, which limits their interaction. In addition, global-level RGB-Event fusion fails to fully exploit fine-grained discriminative cues. To address these issues, we propose Paths, a unified framework with spatio-temporal modeling and hierarchical multi-modal fusion for RE-VReID. Specifically, we first design a Memory-Augmented Backbone (MAB) to maintain modality-specific identity prototypes for stable intra-modal representation learning. Then, we propose a Prompt-aware Spatio-temporal Transformer (PST) to jointly model spatial and temporal cues within a unified Transformer. Finally, we introduce a Hierarchical Multi-modal Fusion (HMF) to integrate RGB and event features at global and local levels. With these modules, our framework can learn robust and discriminative representations for RE-VReID. Extensive experiments on three public RE-VReID benchmarks including EvReID, MARS and iLIDS-VID, demonstrate the effectiveness of our proposed method. The code is available at https://github.com/Reflection0427/Paths.
CommentsAccepted by ACM MM2026. More modifications may be performed
