Abstract
Generalizable image-based person re-identification (Re-ID) aims to recognize individuals across cameras in unseen domains without retraining. While multiple existing approaches address the domain gap through complex architectures, recent findings indicate that better generalization can be achieved by stylistically diverse single-camera data. Although this data is easy to collect, it lacks complexity due to minimal cross-view variation. We propose ReText, a novel method trained on a mixture of multi-camera Re-ID data and single-camera data, where the latter is complemented by textual descriptions to enrich semantic cues. During training, ReText jointly optimizes three tasks: (1) Re-ID on multi-camera data, (2) image-text matching, and (3) image reconstruction guided by text on single-camera data. Experiments demonstrate that ReText achieves strong generalization and significantly outperforms state-of-the-art methods on cross-domain Re-ID benchmarks. To the best of our knowledge, this is the first work to explore multimodal joint learning on a mixture of multi-camera and single-camera data in image-based person Re-ID.
Abstract (translated)
基于图像的人再识别（Re-ID）的泛化目标是跨不同摄像机在未见过的领域中识别个人，而无需重新训练。虽然目前有许多方法通过复杂的架构来解决域差距问题，但最近的研究表明，使用风格多样化的单摄像头数据可以实现更好的泛化效果。尽管这种数据容易收集，但由于视图变化较小，它缺乏复杂性。 为此，我们提出了ReText这一新方法，该方法是在多摄像机Re-ID数据和通过文本描述补充的单摄像头数据混合体上训练的。通过这种方式，在单摄像头数据中添加了丰富的语义线索。在训练过程中，ReText同时优化三个任务：（1）基于多摄像机数据的人再识别；（2）图像与文本匹配；以及（3）由文本指导的单摄像头数据中的图像重建。 实验表明，ReText在跨域人再识别基准测试中表现出强大的泛化能力，并显著优于现有方法。据我们所知，这是首次探索基于图像的人再识别任务中，在多摄像机和单摄像机数据混合体上的多模态联合学习的工作。
URL
https://arxiv.org/abs/2602.05785
