通过频域增强与伪锚点引导，提升红外可见光行人重识别性能。
Spectral Enhancement and Pseudo-Anchor Guidance for Infrared-Visible Person Re-Identification
- 基于频域与灰度空间的同质化光谱增强，减少模态转换损失。
- 引入伪锚点引导双向聚合损失，有效对齐局部模态差异。
- 在两个公开数据集上超越现有方法，适合夜间监控场景应用。
深度学习的发展推动了行人重识别（ReID）技术在智能安防中的应用。可见光-红外行人重识别（VI-ReID）旨在跨模态匹配红外与可见光图像中的行人，实现全天候监控。当前方法依赖无监督模态转换及低效嵌入约束来弥合红外与可见光图像间的光谱差异，但限制了性能潜力。为此，本文提出一种简单而有效的光谱增强与伪锚点引导网络（SEPG-Net）。具体而言，设计了一种基于频率域信息与灰度空间的同质化光谱增强方案，避免传统模态转换导致的信息损失。进一步，提出伪锚点引导的双向聚合（PABA）损失，以更好地对齐局部模态差异并保留判别性身份特征。在两个公开基准数据集上的实验表明，SEPG-Net显著优于其他先进方法。代码已开源：https://github.com/1024AILab/ReID-SEPG。
原文摘要 · Abstract (English)
The development of deep learning has facilitated the application of person re-identification (ReID) technology in intelligent security. Visible-infrared person re-identification (VI-ReID) aims to match pedestrians across infrared and visible modality images enabling 24-hour surveillance. Current studies relying on unsupervised modality transformations as well as inefficient embedding constraints to bridge the spectral differences between infrared and visible images, however, limit their potential performance. To tackle the limitations of the above approaches, this paper introduces a simple yet effective Spectral Enhancement and Pseudo-anchor Guidance Network, named SEPG-Net. Specifically, we propose a more homogeneous spectral enhancement scheme based on frequency domain information and greyscale space, which avoids the information loss typically caused by inefficient modality transformations. Further, a Pseudo Anchor-guided Bidirectional Aggregation (PABA) loss is introduced to bridge local modality discrepancies while better preserving discriminative identity embeddings. Experimental results on two public benchmark datasets demonstrate the superior performance of SEPG-Net against other state-of-the-art methods. The code is available at https://github.com/1024AILab/ReID-SEPG.
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