发表机构
School of Computer Science and Technology, Xinjiang University; Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University（新疆大学计算机科学与技术学院; 新疆大学丝绸之路多语言认知计算联合国际研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对可见光-红外行人重识别的跨模态差异与判别能力不足问题，提出MDCRNet网络，结合HDCA模块与JDML损失，在SYSU-MM01和RegDB数据集上取得最优性能。
AI 中文摘要
可见光-红外行人重识别（VI-ReID）存在跨模态差异和判别能力有限的问题，导致识别性能欠佳。现有方法在语义挖掘、跨模态融合和特征约束方面存在局限。为应对这些挑战，我们提出MDCRNet，即多尺度分解卷积细化网络，用于增强跨模态特征学习和判别度量学习。具体而言，我们引入包含4个分层分解卷积注意力（HDCA）模块的分层学习模块（HLM），每个模块配备轻量通道注意力和多尺度空间感知块以捕捉多尺度空间依赖关系。此外，我们开发联合判别度量损失（JDML），其包含新颖的粒度判别损失（GDL），可同时优化跨模态下的身份内紧凑性和身份间可分性。在SYSU-MM01和RegDB数据集上的大量实验表明，MDCRNet在两个基准上均达到了当前最优性能，代码可在该https网址获取。
英文摘要
Visible-infrared person re-identification (VI-ReID) suffers from cross-modal discrepancies and limited discriminative capabilities, leading to suboptimal recognition performance. Current approaches exhibit limitations in semantic mining, cross-modal fusion and feature constraints. To tackle these challenges, we propose MDCRNet, a Multi-scale Decomposed Convolution Refinement Network that enhances cross-modal feature learning and discriminative metric learning. Specifically, we introduce a Hierarchical Learning Module (HLM) containing four Hierarchical Decomposed Convolution Attention (HDCA) modules, each equipped with lightweight channel attention and multi-scale spatial perception blocks to capture multi-scale spatial dependencies. Moreover, we develop a Joint Discriminative Metric Loss (JDML) incorporating a novel Granularity Discriminative Loss (GDL) that simultaneously optimizes intra-identity compactness and inter-identity separability across modalities. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate that MDCRNet achieves state-of-the-art performance on both benchmarks. Code is available at https://github.com/Kevin-zms/MDCRNet.
Comments15 pages, 4 figures. Accepted for publication in the LNCS proceedings of ICONIP 2026
