Abstract
The performance of models is intricately linked to the abundance of training data. In Visible-Infrared person Re-IDentification (VI-ReID) tasks, collecting and annotating large-scale images of each individual under various cameras and modalities is tedious, time-expensive, costly and must comply with data protection laws, posing a severe challenge in meeting dataset requirements. Current research investigates the generation of synthetic data as an efficient and privacy-ensuring alternative to collecting real data in the field. However, a specific data synthesis technique tailored for VI-ReID models has yet to be explored. In this paper, we present a novel data generation framework, dubbed Diffusion-based VI-ReID data Expansion (DiVE), that automatically obtain massive RGB-IR paired images with identity preserving by decoupling identity and modality to improve the performance of VI-ReID models. Specifically, identity representation is acquired from a set of samples sharing the same ID, whereas the modality of images is learned by fine-tuning the Stable Diffusion (SD) on modality-specific data. DiVE extend the text-driven image synthesis to identity-preserving RGB-IR multimodal image synthesis. This approach significantly reduces data collection and annotation costs by directly incorporating synthetic data into ReID model training. Experiments have demonstrated that VI-ReID models trained on synthetic data produced by DiVE consistently exhibit notable enhancements. In particular, the state-of-the-art method, CAJ, trained with synthetic images, achieves an improvement of about $9\%$ in mAP over the baseline on the LLCM dataset. Code: this https URL
Abstract (translated)
模型的性能与其训练数据的数量密切相关。在可见光-红外行人重识别（VI-ReID）任务中，收集和标注不同摄像头及模态下每个人的大规模图像是一项繁琐、耗时且昂贵的任务，并且必须遵守数据保护法规，这使得满足数据集要求变得极其具有挑战性。当前的研究调查了一种生成合成数据的方法作为实地采集真实数据的高效且隐私保障的替代方案。然而，尚未有专门针对VI-ReID模型的数据合成技术被探索出来。在本文中，我们提出了一种新颖的数据生成框架，称为基于扩散的可见光-红外行人重识别数据扩展（DiVE），该框架能够通过解耦身份和模态来自动获取大量具有身份保持性的RGB-IR配对图像以提升VI-ReID模型的表现。具体来说，身份表示是从一组具有相同ID的样本中获得的，而图像的模态则是通过对特定模态的数据进行微调稳定扩散（SD）模型得到的。DiVE将文本驱动的图像生成扩展到了保持身份一致性的RGB-IR多模态图像合成。这一方法通过直接在Re-ID模型训练过程中加入合成数据显著减少了数据采集和标注的成本。实验表明，基于DiVE生成的合成数据训练的VI-ReID模型持续表现出明显的性能提升。特别地，在LLCM数据集上使用最先进的方法CAJ进行训练时，与基线相比，其mAP值提高了大约9%。 代码链接：[此URL](https://this https URL)
URL
https://arxiv.org/abs/2503.12472
