Abstract
Person re-identification (ReID) aims to retrieve the images of an interested person in the gallery images, with wide applications in medical rehabilitation, abnormal behavior detection, and public security. However, traditional person ReID models suffer from uni-modal capability, leading to poor generalization ability in multi-modal data, such as RGB, thermal, infrared, sketch images, textual descriptions, etc. Recently, the emergence of multi-modal large language models (MLLMs) shows a promising avenue for addressing this problem. Despite this potential, existing methods merely regard MLLMs as feature extractors or caption generators, which do not fully unleash their reasoning, instruction-following, and cross-modal understanding capabilities. To bridge this gap, we introduce MMReID-Bench, the first multi-task multi-modal benchmark specifically designed for person ReID. The MMReID-Bench includes 20,710 multi-modal queries and gallery images covering 10 different person ReID tasks. Comprehensive experiments demonstrate the remarkable capabilities of MLLMs in delivering effective and versatile person ReID. Nevertheless, they also have limitations in handling a few modalities, particularly thermal and infrared data. We hope MMReID-Bench can facilitate the community to develop more robust and generalizable multimodal foundation models for person ReID.
Abstract (translated)
人员重识别（ReID）的目标是从图库图像中检索感兴趣人物的图片，其在医疗康复、异常行为检测和公共安全等领域有广泛应用。然而，传统的人员ReID模型存在单一模态能力的问题，导致其在RGB、热成像、红外、素描图像以及文本描述等多模态数据上的泛化能力较差。最近，多模态大型语言模型（MLLMs）的出现为解决这一问题提供了有前景的方法。尽管潜力巨大，现有方法仅将MLLMs视为特征提取器或标题生成器，未能充分发挥其推理、指令遵循及跨模态理解的能力。为弥补这一差距，我们引入了MMReID-Bench，这是首个专门为人员ReID设计的多任务多模态基准平台。MMReID-Bench包含了20,710个多模态查询和图库图片，并涵盖了10种不同的人员ReID任务。综合实验展示了MLLMs在提供有效且多功能人员ReID方面的显著能力，然而它们在处理某些特定模式数据（尤其是热成像和红外数据）时仍存在局限性。我们希望通过MMReID-Bench能够促进社区开发出更加稳健且具有泛化性的多模态基础模型以应对人员ReID挑战。
URL
https://arxiv.org/abs/2508.06908
