IEEE TPAMI 2025 | 北京大学提出分布驱动的终身学习范式，用结构建模解决灾难性遗忘

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].