Abstract
Person re-identification (ReID) has evolved from handcrafted feature-based methods to deep learning approaches and, more recently, to models incorporating large language models (LLMs). Early methods struggled with variations in lighting, pose, and viewpoint, but deep learning addressed these issues by learning robust visual features. Building on this, LLMs now enable ReID systems to integrate semantic and contextual information through natural language. This survey traces that full evolution and offers one of the first comprehensive reviews of ReID approaches that leverage LLMs, where textual descriptions are used as privileged information to improve visual matching. A key contribution is the use of dynamic, identity-specific prompts generated by GPT-4o, which enhance the alignment between images and text in vision-language ReID systems. Experimental results show that these descriptions improve accuracy, especially in complex or ambiguous cases. To support further research, we release a large set of GPT-4o-generated descriptions for standard ReID datasets. By bridging computer vision and natural language processing, this survey offers a unified perspective on the field's development and outlines key future directions such as better prompt design, cross-modal transfer learning, and real-world adaptability.
Abstract (translated)
人员重新识别（ReID）技术已经从基于手工制作特征的方法演进到深度学习方法，再到最近结合大型语言模型（LLMs）的模型。早期方法在处理光照、姿态和视角变化时遇到了困难，但深度学习通过学习鲁棒的视觉特征解决了这些问题。在此基础上，LLMs使得ReID系统能够通过自然语言整合语义和上下文信息。本次调查追溯了这一完整的发展历程，并对利用LLMs增强人员重新识别的方法进行了迄今为止最全面的回顾之一，其中文本描述被用作特权信息来提升视觉匹配性能。一个关键贡献是使用GPT-4o生成的身份特定动态提示，这增强了图像与文字之间的对齐，在视觉语言ReID系统中尤为有效。实验结果显示，这些描述在复杂或模棱两可的情况下特别提升了准确度。为了支持进一步的研究，我们发布了一大批针对标准ReID数据集的由GPT-4o生成的描述。通过将计算机视觉和自然语言处理领域结合在一起，这项调查提供了一个统一视角来审视该领域的进展，并概述了未来的关键发展方向，如更好的提示设计、跨模态迁移学习以及现实世界的适应性。
URL
https://arxiv.org/abs/2506.13039
