Lifelong Visible-Infrared Person Re-Identification with Prompt Pool and Instance-level Prompt Generator
Zhenxi Luo, Guoqiang Xiao, Michael S. Lew, Song Wu · 2025
Most existing Visible-Infrared Person Re-Identification (VI-ReID) methods primarily rely on fixed datasets for training, which struggle to accommodate continuously evolving cross-domain data, thus significantly limiting the adaptation in real-world dynamic scenarios. The task of Lifelong Visible-Infrared Person Re-Identification (LVI-ReID) emerged and is required to overcome the challenge of the semantic gap caused by both cross-modality and cross-domain data. Drawing inspiration from complementary learning systems, we propose a prompt-based dynamic learning framework to address the challenges inherent in LVI-ReID. Specifically, we design a Prompt Pool (PP) module to encapsulate shared knowledge across tasks or domains. In addition, we propose an instance-level prompt generator (IPG) to further enhance the model's ability to capture domain-specific knowledge, overcoming the limitations of a fixed-size prompt pool. For task-agnostic inference during the LVI-ReID phase, we develop a query-key mechanism that adaptively selects the most relevant prompt by evaluating the similarity between query tokens and keys, thereby addressing the nuanced requirements of varying tasks. Extensive experimental evaluations demonstrate the superiority of our proposed prompt learning-based PP-IPG framework over state-of-the-art methods in both lifelong learnings, lifelong person re-identification (LReID), and LVI-ReID settings. These results underscore the efficacy and practicality of our framework for advancing LVI-ReID across dynamic cross-modality and cross-domains. The source code of our designed PP-IPG method is at https://github.com/SWU-CSMediaLab/PP-IPG.