AAAI 2026｜新突破：北大彭宇新团队提出可见光-红外终身行人重识别方法CKDA

Core Insights - The article discusses the development of a novel method for lifelong pedestrian re-identification called CKDA, which aims to continuously learn new discriminative information from incoming data while retaining the ability to recognize known data across different modalities, specifically visible light and infrared images [2][6]. Group 1: Background and Motivation - Lifelong pedestrian re-identification focuses on recognizing the same individual across different scenarios by continuously learning from pedestrian data collected in various environments [6]. - Existing methods struggle to balance the acquisition of modality-specific knowledge and the retention of cross-modal common knowledge, leading to conflicts that hinder effective learning [9][11]. Group 2: Technical Solution - The CKDA method introduces a framework that includes three main modules: 1. Cross-modal common prompts, which extract shared discriminative knowledge by removing style information unique to each modality [12]. 2. Single-modal specific prompts, which enhance the retention of knowledge specific to each modality while avoiding interference between modalities [20]. 3. Cross-modal knowledge alignment, which aligns new and old knowledge in independent feature spaces to improve the model's ability to balance discriminative knowledge across modalities [24][25]. Group 3: Experimental Results - The CKDA method achieved the best performance on four commonly used visible-infrared pedestrian re-identification datasets, with an average mAP of 36.3% and R1 accuracy of 39.4% [28]. - The results indicate that CKDA effectively enhances the model's perception and retention capabilities for both visible and infrared modality information through complementary prompts [30][29].