Abstract
Person re-identification (Re-ID) is a critical task in human-centric intelligent systems, enabling consistent identification of individuals across different camera views using multi-modal query information. Recent studies have successfully integrated LVLMs with person Re-ID, yielding promising results. However, existing LVLM-based methods face several limitations. They rely on extracting textual embeddings from fixed templates, which are used either as intermediate features for image representation or for prompt tuning in domain-specific tasks. Furthermore, they are unable to adopt the VQA inference format, significantly restricting their broader applicability. In this paper, we propose a novel, versatile, one-for-all person Re-ID framework, ChatReID. Our approach introduces a Hierarchical Progressive Tuning (HPT) strategy, which ensures fine-grained identity-level retrieval by progressively refining the model's ability to distinguish pedestrian identities. Extensive experiments demonstrate that our approach outperforms SOTA methods across ten benchmarks in four different Re-ID settings, offering enhanced flexibility and user-friendliness. ChatReID provides a scalable, practical solution for real-world person Re-ID applications, enabling effective multi-modal interaction and fine-grained identity discrimination.
Abstract (translated)
人员再识别（Re-ID）是基于人类的智能系统中的一个关键任务，它能够利用多模态查询信息在不同的摄像机视角下持续地对个人进行一致的身份识别。最近的研究成功地将大型视觉语言模型（LVLMs）与人员Re-ID结合在一起，取得了很有前景的结果。然而，现有的基于LVLM的方法面临多个限制。它们依赖于从固定模板中提取文本嵌入，并将其用作图像表示的中间特征或用于特定领域的提示微调。此外，这些方法无法采用视觉问答（VQA）推理格式，这极大地限制了它们在更广泛的应用场景中的适用性。 在这篇论文中，我们提出了一种新颖、多功能且适用于所有情况的人员Re-ID框架——ChatReID。我们的方法引入了一个分层逐步微调（Hierarchical Progressive Tuning, HPT）策略，通过逐步提高模型区分行人身份的能力，确保了精细粒度的身份级检索。广泛的实验表明，在四个不同的Re-ID设置下的十个基准测试中，我们提出的方法优于现有的最先进方法，提供了增强的灵活性和用户友好性。ChatReID为现实世界中的人员Re-ID应用提供了一个可扩展且实用的解决方案，支持有效的多模态交互与精细粒度的身份区分。
URL
https://arxiv.org/abs/2502.19958
