arXiv 2409.199542025-09-03cs.CVcs.AI

面向终身行人重识别的域一致性表征学习

Domain Consistency Representation Learning for Lifelong Person Re-Identification

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；College of Automation Science and Engineering, South China University of Technology（自动化科学与工程学院，华南理工大学）

Shiben Liu, Huijie Fan, Qiang Wang, Weihong Ren, Yandong Tang, Yang Cong

备注：: 12 pages, 7 figures

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AI总结：

针对终身行人重识别中域内判别性与域间差距难以平衡的问题，提出域一致性表征学习模型，结合全局与属性表征及抗遗忘、知识巩固策略，实现更优的重识别性能。

摘要：

终身行人重识别（LReID）在从连续数据中学习时，域内判别性与域间差距存在矛盾关系。域内判别性关注个体细微差异（如服装类型、配饰等），而域间差距则强调域一致性。在最大化域内判别性与最小化域间差距之间取得平衡，是提升LReID性能的关键挑战。现有大多数方法通过知识蒸馏缩小域间差距以维持域一致性，但往往忽略了域内判别性。为解决这一挑战，我们提出一种新型域一致性表征学习（DCR）模型，以全局表征和属性级表征为桥梁平衡域内判别性与域间差距。在域内层面，我们挖掘全局表征与属性级表征的互补关系，以提升相似身份间的判别性。过度学习域内判别性会导致灾难性遗忘，我们进一步提出属性导向的抗遗忘（AF）策略，利用属性级表征增强域间一致性，并提出知识巩固（KC）策略促进知识迁移。大量实验表明，我们的DCR模型相比现有最优LReID方法取得了更优性能。代码可在https://github.com/LiuShiBen/DCR获取。

英文摘要：

Lifelong person re-identification (LReID) exhibits a contradictory relationship between intra-domain discrimination and inter-domain gaps when learning from continuous data. Intra-domain discrimination focuses on individual nuances (i.e., clothing type, accessories, etc.), while inter-domain gaps emphasize domain consistency. Achieving a trade-off between maximizing intra-domain discrimination and minimizing inter-domain gaps is a crucial challenge for improving LReID performance. Most existing methods strive to reduce inter-domain gaps through knowledge distillation to maintain domain consistency. However, they often ignore intra-domain discrimination. To address this challenge, we propose a novel domain consistency representation learning (DCR) model that explores global and attribute-wise representations as a bridge to balance intra-domain discrimination and inter-domain gaps. At the intra-domain level, we explore the complementary relationship between global and attribute-wise representations to improve discrimination among similar identities. Excessive learning intra-domain discrimination can lead to catastrophic forgetting. We further develop an attribute-oriented anti-forgetting (AF) strategy that explores attribute-wise representations to enhance inter-domain consistency, and propose a knowledge consolidation (KC) strategy to facilitate knowledge transfer. Extensive experiments show that our DCR achieves superior performance compared to state-of-the-art LReID methods. Our code is available at https://github.com/LiuShiBen/DCR.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。