Unsupervised domain adaptive person re-identification (Re-ID) methods alleviate the burden of data annotation through generating pseudo supervision messages. However, real-world Re-ID systems, with continuously accumulating data streams, simultaneously demand more robust adaptation and anti-forgetting capabilities. Methods based on image rehearsal addresses the forgetting issue with limited extra storage but carry the risk of privacy leakage. In this work, we propose a Color Prompting (CoP) method for data-free continual unsupervised domain adaptive person Re-ID. Specifically, we employ a light-weighted prompter network to fit the color distribution of the current task together with Re-ID training. Then for the incoming new tasks, the learned color distribution serves as color style transfer guidance to transfer the images into past styles. CoP achieves accurate color style recovery for past tasks with adequate data diversity, leading to superior anti-forgetting effects compared with image rehearsal methods. Moreover, CoP demonstrates strong generalization performance for fast adaptation into new domains, given only a small amount of unlabeled images. Extensive experiments demonstrate that after the continual training pipeline the proposed CoP achieves 6.7% and 8.1% average rank-1 improvements over the replay method on seen and unseen domains, respectively. The source code for this work is publicly available in https://github.com/vimar-gu/ColorPromptReID.

翻译：无监督域自适应行人重识别（Re-ID）方法通过生成伪监督信息来减轻数据标注的负担。然而，实际Re-ID系统面临持续累积的数据流，同时需要更强的自适应能力和抗遗忘能力。基于图像回放的方法以有限额外存储解决了遗忘问题，但存在隐私泄露风险。本文提出一种名为颜色提示（CoP）的无数据持续无监督域自适应行人重识别方法。具体而言，我们采用轻量级提示网络在Re-ID训练过程中拟合当前任务的颜色分布。随后，对于新到来的任务，学到的颜色分布作为颜色风格迁移指导，将图像迁移至过去风格。CoP在保持充分数据多样性的前提下，实现了对过去任务的精确颜色风格恢复，相较于图像回放方法展现出更优的抗遗忘效果。此外，CoP仅需少量无标签图像即可快速适应新域，展现出强大的泛化性能。大量实验表明，在持续训练流程后，所提出的CoP在已见域和未见域上分别比回放方法平均提升6.7%和8.1%的Rank-1准确率。本工作源代码已公开于https://github.com/vimar-gu/ColorPromptReID。