论文链接：http://arxiv.org/abs/2511.15016 代码仓库：https://github.com/PKU-ICST-MIPL/CKDA-AAAI2026 终身行人重识别 旨在持续学习新增数据中不断涌现的新增行人鉴别性信息，同时保持对已知数据的识别能力，在公共安防、社区管理、运动分析等场景中具有重 要的研究和应用价值。 随着白天可见光图像和夜晚红外图像被不断采集，现有终身行人重识别方法需要持续学习特定模态中的新知识（例如：仅适用于红外模态中的热辐射信息）。 然而，特定模态中新知识的学习过程阻碍了模态间公共旧知识（例如：同时适用于可见光与红外模态的人体体态信息）的保留，导致了单模态专用知识的获取与 跨模态公共知识的保留间的冲突，进而限制了持续学习场景下平衡不同模态中行人鉴别性知识的能力。 针对这一问题， 北京大学彭宇新教授团队 提出了 跨模态知识解耦与对齐的可见光 - 红外终身行人重识别方法 CKDA， 通过跨模态通用提示模块与单模态专用提 示模块显式地解耦并净化不同模态通用与特定模态专用的鉴别性信息，从而避免二者间的相互干扰，并在一对彼此独立的模态内与模态间特征空间中分别对齐解 耦后的新 ...

Core Viewpoint - The article discusses a recent research achievement in the field of artificial intelligence, specifically focusing on a new framework called DKP++ for lifelong person re-identification (LReID), which addresses the catastrophic forgetting problem in lifelong learning by enhancing memory retention of historical knowledge and improving cross-domain learning capabilities [2][3]. Research Background - Person re-identification (ReID) aims to match and associate images of the same individual across different camera views, locations, and times, with applications in surveillance, intelligent transportation, and urban safety management [3]. - The traditional ReID paradigm struggles with domain shift issues due to variations in data collection conditions, leading to inadequate adaptability in long-term dynamic environments [3]. Research Challenges - The core challenge in lifelong person re-identification is the catastrophic forgetting problem, where the model's retrieval performance for old domain data significantly decreases after learning new domain knowledge [5]. - Existing methods to mitigate forgetting, such as retaining historical samples or using knowledge distillation, face limitations related to data privacy risks, storage overhead, and model flexibility [5]. Research Motivation - The motivation behind DKP++ includes distribution-aware prototype learning to effectively retain historical knowledge without storing historical samples, and cross-domain distribution alignment to enhance the model's ability to learn new knowledge while utilizing historical information [8][10]. Method Design - DKP++ employs a distribution-aware knowledge aligning and prototyping framework, which includes: 1. Instance-level fine-grained modeling to capture local details of person instances [14]. 2. Distribution-aware prototype generation to create robust category-level prototypes that retain intra-class variation knowledge [14]. 3. Distribution alignment to bridge the feature distribution gap between new and old data [14]. 4. Prototype-based knowledge transfer to guide model learning using generated prototypes and labeled new data [14]. Experimental Analysis - The experiments utilized two typical training domain sequences and five widely used ReID datasets, evaluating the model's knowledge retention and generalization capabilities [16]. - DKP++ demonstrated an improvement of 5.2%-7% in average performance on known domains and 4.5%-7.7% in overall generalization performance on unseen domains compared to existing methods [17]. - The model showed higher historical knowledge retention and faster performance growth in unseen domains as the number of learned domains increased [20]. Technical Innovations - DKP++ introduces innovative designs focusing on distribution prototype modeling and representation, as well as sample alignment-guided prototype knowledge transfer to overcome distribution gaps between new and old domain data [23]. Future Outlook - The research suggests potential improvements in areas such as distribution alignment using larger models, active forgetting mechanisms to eliminate redundant knowledge, and multi-modal lifelong learning capabilities to enhance perception in complex environments [23].

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].