Abstract
Text-to-image person re-identification (ReID) aims to retrieve the images of an interested person based on textual descriptions. One main challenge for this task is the high cost in manually annotating large-scale databases, which affects the generalization ability of ReID models. Recent works handle this problem by leveraging Multi-modal Large Language Models (MLLMs) to describe pedestrian images automatically. However, the captions produced by MLLMs lack diversity in description styles. To address this issue, we propose a Human Annotator Modeling (HAM) approach to enable MLLMs to mimic the description styles of thousands of human annotators. Specifically, we first extract style features from human textual descriptions and perform clustering on them. This allows us to group textual descriptions with similar styles into the same cluster. Then, we employ a prompt to represent each of these clusters and apply prompt learning to mimic the description styles of different human annotators. Furthermore, we define a style feature space and perform uniform sampling in this space to obtain more diverse clustering prototypes, which further enriches the diversity of the MLLM-generated captions. Finally, we adopt HAM to automatically annotate a massive-scale database for text-to-image ReID. Extensive experiments on this database demonstrate that it significantly improves the generalization ability of ReID models.
Abstract (translated)
文本到图像的人再识别（ReID）旨在根据文字描述检索感兴趣人物的图片。这一任务的主要挑战之一是大规模数据库的手动标注成本高昂，这影响了ReID模型的泛化能力。近期的研究通过利用多模态大型语言模型（MLLMs）自动描述行人图像来解决这个问题。然而，由MLLM生成的说明缺乏多样化的描述风格。为了解决这一问题，我们提出了一种人类标注员建模（HAM）方法，使MLLM能够模仿数千名人类标注员的描述风格。具体来说，我们首先从人类文本描述中提取风格特征，并对其执行聚类操作，以便将具有相似风格的文字描述归入同一类别。然后，为每个这样的集群使用一个提示来表示它，并应用提示学习以模仿不同的人类标注员的描述风格。此外，我们定义了一个风格特征空间并在该空间内进行均匀采样，从而获取更多的多样化聚类原型，进一步丰富了MLLM生成的说明多样性。最后，我们将HAM应用于大规模数据库的自动注释，用于文本到图像的ReID任务。在这一数据库上的广泛实验表明，这显著提高了ReID模型的泛化能力。
URL
https://arxiv.org/abs/2503.09962
