arXiv 2508.069082025-11-23cs.CVcs.AI

找到它们全部：揭示MLLMs用于多功能人物重识别

Find Them All: Unveiling MLLMs for Versatile Person Re-identification

School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Institute of Image Communication and Information Processing, Shanghai Jiao Tong University（上海交通大学图像通信与信息处理研究所）；Macao Polytechnic University（澳门 polytechnic 大学）；Shanghai AI Laboratory（上海人工智能实验室）

Jinhao Li, Zijian Chen, Lirong Deng, Guangtao Zhai, Changbo Wang

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AI总结：

本文提出VP-ReID基准，利用MLLMs提升人物重识别的多功能性和有效性，同时揭示其在处理某些模态时的局限性。

摘要：

人物重识别（ReID）旨在从画廊集中检索目标人物的图像，广泛应用于医疗康复和公共安全领域。然而，传统的人物ReID模型通常是单模态的，导致在异构数据模态中泛化能力有限。最近，多模态大语言模型（MLLMs）的出现为解决这一问题提供了有希望的途径。尽管有这种潜力，现有方法仅将MLLMs视为特征提取器或描述生成器，使其在人物ReID任务中的能力未被充分探索。为填补这一空白，我们引入了一个新的基准，称为多功能人物重识别（VP-ReID）。该基准包括257,310个多模态查询和画廊图像，涵盖十种多样化的ReID任务。此外，我们还提出了两种面向任务的评估方案用于基于MLLMs的人物ReID。广泛的实验表明，MLLMs在各种人物ReID任务中表现出卓越的多功能性、有效性和可解释性。然而，它们在处理某些模态时也存在局限性，特别是热成像和红外数据。我们希望VP-ReID能够促进社区在开发更稳健和通用的跨模态基础模型方面的发展。

英文摘要：

Person re-identification (ReID) aims to retrieve images of a target person from the gallery set, with wide applications in medical rehabilitation and public security. However, traditional person ReID models are typically uni-modal, resulting in limited generalizability across heterogeneous data modalities. Recently, the emergence of multi-modal large language models (MLLMs) has shown a promising avenue for addressing this issue. Despite this potential, existing methods merely regard MLLMs as feature extractors or caption generators, leaving their capabilities in person ReID tasks largely unexplored. To bridge this gap, we introduce a novel benchmark for \underline{\textbf{V}}ersatile \underline{\textbf{P}}erson \underline{\textbf{Re}}-\underline{\textbf{ID}}entification, termed VP-ReID. The benchmark includes 257,310 multi-modal queries and gallery images, covering ten diverse person ReID tasks. In addition, we propose two task-oriented evaluation schemes for MLLM-based person ReID. Extensive experiments demonstrate the impressive versatility, effectiveness, and interpretability of MLLMs in various person ReID tasks. Nevertheless, they also have limitations in handling a few modalities, particularly thermal and infrared data. We hope that VP-ReID can facilitate the community in developing more robust and generalizable cross-modal foundation models for person ReID.

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