近日，北京大学王选计算机研究所周嘉欢助理教授与彭宇新教授合作在人工智能重要国际期刊 IEEE TPAMI 发布一项最新的研究成果： DKP++（Distribution- aware Knowledge Aligning and Prototyping for Non-exemplar Lifelong Person Re-Identification） 。该工作针对终身学习中的灾难性遗忘问题，提出分布建模引导 的知识对齐与原型建模框架，不仅有效增强了对历史知识的记忆能力，也提升了模型的跨域学习能力。 本文的第一作者为北京大学北京大学王选计算机研究所助理教授周嘉欢，通讯作者为北京大学王选计算机研究所教授彭宇新。目前该研究已被 IEEE TPAMI 接 收，相关代码已开源。 行人重识别（Person Re-Identification, ReID）旨在针对跨相机视角、跨地点、跨时间等场景中，基于视觉特征实现对同一行人图像的匹配与关联。该技术在多摄像 头监控、智能交通系统、城市安全管理以及大规模图像视频检索等实际场景中具有广泛应用价值。然而，在现实环境中，由于采集地点、拍摄设备和时间条件的 不断变化，行人图像的分 ...

Core Viewpoint - The article discusses the introduction of LSTKC++, a framework developed by a research team at Peking University, aimed at addressing the challenges of lifelong person re-identification (LReID) by effectively balancing the learning of new knowledge and the retention of historical knowledge [1][2][9]. Summary by Sections 1. Introduction to LSTKC++ - LSTKC++ incorporates long-short term knowledge decoupling and dynamic correction mechanisms to enhance the model's ability to learn new knowledge while retaining historical knowledge during lifelong learning [2][9]. 2. Challenges in Person Re-Identification - The traditional ReID paradigm struggles with domain shifts due to variations in location, equipment, and time, leading to difficulties in adapting to long-term dynamic changes in test data [5][6]. - The core challenge of LReID is the catastrophic forgetting problem, where the model's performance on old domain data degrades after learning new domain knowledge [9][12]. 3. Framework Overview - The LSTKC framework, introduced in AAAI 2024, divides the lifelong learning model into short-term and long-term models, allowing for a collaborative fusion of knowledge [11]. - LSTKC++ improves upon LSTKC by decoupling the long-term model into two parts: one for earlier historical knowledge and another for more recent historical knowledge [19]. 4. Methodology Enhancements - LSTKC++ features complementary knowledge transfer between long-term and short-term models, correcting knowledge based on sample affinity matrices and optimizing knowledge weighting parameters using new domain training data [19][20]. - The framework introduces a sample relationship-guided long-term knowledge consolidation mechanism to facilitate reasoning with both long-term and short-term knowledge after learning new domains [20]. 5. Experimental Analysis - LSTKC++ shows an improvement in known domain average performance (Seen-Avg mAP and Seen-Avg R@1) by 1.5%-3.4% compared to existing methods, and an increase in overall generalization performance (Unseen-Avg mAP and Unseen-Avg R@1) by 1.3%-4% [25]. - The framework demonstrates significant performance advantages in intermediate domains while maintaining a balance between knowledge retention and learning new information [25]. 6. Technical Innovations - The work focuses on the challenges of new knowledge learning and historical knowledge forgetting, proposing innovative designs such as a decoupled knowledge memory system and a semantic-level knowledge correction mechanism [26]. 7. Practical Applications - LSTKC++ is suitable for dynamic environments where camera deployment conditions frequently change, making it applicable in smart cities, edge computing, and security scenarios [27]. - The framework is designed to meet privacy protection needs by not requiring access to historical samples during the continuous learning process [27]. 8. Future Directions - Future research may explore extending LSTKC++ to pre-trained visual large models and integrating multi-modal perception for enhanced stability and robustness in continuous learning [28].