Abstract
Lifelong Person Re-identification (LReID) suffers from a key challenge in preserving old knowledge while adapting to new information. The existing solutions include rehearsal-based and rehearsal-free methods to address this challenge. Rehearsal-based approaches rely on knowledge distillation, continuously accumulating forgetting during the distillation process. Rehearsal-free methods insufficiently learn the distribution of each domain, leading to forgetfulness over time. To solve these issues, we propose a novel Distribution-aware Forgetting Compensation (DAFC) model that explores cross-domain shared representation learning and domain-specific distribution integration without using old exemplars or knowledge distillation. We propose a Text-driven Prompt Aggregation (TPA) that utilizes text features to enrich prompt elements and guide the prompt model to learn fine-grained representations for each instance. This can enhance the differentiation of identity information and establish the foundation for domain distribution awareness. Then, Distribution-based Awareness and Integration (DAI) is designed to capture each domain-specific distribution by a dedicated expert network and adaptively consolidate them into a shared region in high-dimensional space. In this manner, DAI can consolidate and enhance cross-domain shared representation learning while alleviating catastrophic forgetting. Furthermore, we develop a Knowledge Consolidation Mechanism (KCM) that comprises instance-level discrimination and cross-domain consistency alignment strategies to facilitate model adaptive learning of new knowledge from the current domain and promote knowledge consolidation learning between acquired domain-specific distributions, respectively. Experimental results show that our DAFC outperform state-of-the-art methods by at least 9.8\%/6.6\% and 6.4\%/6.2\% of average mAP/R@1 on two training orders.
Abstract (translated)
终身人员再识别（LReID）面临着一个关键挑战，即在适应新信息的同时保持旧知识。现有的解决方案包括基于重播的方法和无重播方法来应对这一挑战。基于重播的方法依赖于知识蒸馏，在这个过程中会不断积累遗忘。而无需重播的方法未能充分学习每个领域的分布，导致随着时间的推移出现记忆丧失问题。为了解决这些问题，我们提出了一种新颖的分布感知遗忘补偿（DAFC）模型，该模型探索了跨域共享表示学习以及领域特定分布整合，而不使用旧样本或知识蒸馏。 此外，我们提出了文本驱动的提示聚合(TPA)，利用文本特征丰富提示元素，并引导提示模型为每个实例学习精细粒度表示。这可以增强身份信息的区别性，并为领域分布感知建立基础。然后设计了基于分布的意识和整合(DAI)机制，通过专用专家网络捕捉每个领域的特定分布并自适应地将它们合并到高维空间中的共享区域中。以此方式，DAI能够在缓解灾难性遗忘的同时巩固和增强跨域共享表示学习。 此外，我们开发了一种知识固化机制(KCM)，该机制包含实例级区分策略和跨领域一致性对齐策略，以促进模型从当前领域适应性地学习新知识，并推动已获得领域的特定分布之间的知识整合学习。 实验结果表明，在两种训练顺序下，我们的DAFC比最先进的方法至少提高了9.8%/6.6%（平均mAP/R@1）和6.4%/6.2%的性能。
URL
https://arxiv.org/abs/2504.15041
