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].