Abstract
Current lifelong person re-identification (LReID) methods predominantly rely on fully labeled data streams. However, in real-world scenarios where annotation resources are limited, a vast amount of unlabeled data coexists with scarce labeled samples, leading to the Semi-Supervised LReID (Semi-LReID) problem where LReID methods suffer severe performance degradation. Existing LReID methods, even when combined with semi-supervised strategies, suffer from limited long-term adaptation performance due to struggling with the noisy knowledge occurring during unlabeled data utilization. In this paper, we pioneer the investigation of Semi-LReID, introducing a novel Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation framework (SPRED). Our key innovation lies in establishing a self-reinforcing cycle between dynamic prototype-guided pseudo-label generation and new-old knowledge collaborative purification to enhance the utilization of unlabeled data. Specifically, learnable identity prototypes are introduced to dynamically capture the identity distributions and generate high-quality pseudo-labels. Then, the dual-knowledge cooperation scheme integrates current model specialization and historical model generalization, refining noisy pseudo-labels. Through this cyclic design, reliable pseudo-labels are progressively mined to improve current-stage learning and ensure positive knowledge propagation over long-term learning. Experiments on the established Semi-LReID benchmarks show that our SPRED achieves state-of-the-art performance. Our source code is available at this https URL
Abstract (translated)
当前的人终身重识别（LReID）方法主要依赖于完全标注的数据流。然而，在实际场景中，由于注释资源有限，大量的未标记数据与稀少的已标记样本并存，导致了半监督人终身重识别（Semi-LReID）问题：在这种情况下，LReID 方法的表现会严重下降。现有的 LReID 方法即使结合了半监督策略，也因为无法妥善处理在使用未标注数据时产生的噪声知识而长期适应性表现有限。 本文首次研究了 Semi-LReID 问题，并提出了一种新颖的自强化原型进化与双知识合作框架（SPRED）。我们的关键创新在于建立了动态原型引导伪标签生成和新旧知识协同净化之间的自我增强循环，以提高未标注数据的利用效率。具体而言，我们引入可学习的身份原型来动态捕捉身份分布并生成高质量的伪标签。接着，双知识合作方案整合了当前模型的专业化能力和历史模型的泛化能力，从而精炼出噪声伪标签。通过这种循环设计，SPRED 能够逐步挖掘可靠伪标签以提升现阶段的学习效果，并确保长期学习中积极的知识传播。 在建立的 Semi-LReID 基准上进行的实验表明，我们的 SPRED 达到了最先进的性能水平。源代码可在提供的链接获取（假设该链接指向一个公开可用的位置）。
URL
https://arxiv.org/abs/2507.01884
