Text-based Person Retrieval (TPR) aims to retrieve person images that match
the description given a text query. The performance improvement of the TPR
model relies on high-quality data for supervised training. However, it is
difficult to construct a large-scale, high-quality TPR dataset due to expensive
annotation and privacy protection. Recently, Large Language Models (LLMs) have
approached or even surpassed human performance on many NLP tasks, creating the
possibility to expand high-quality TPR datasets. This paper proposes an
LLM-based Data Augmentation (LLM-DA) method for TPR. LLM-DA uses LLMs to
rewrite the text in the current TPR dataset, achieving high-quality expansion
of the dataset concisely and efficiently. These rewritten texts are able to
increase the diversity of vocabulary and sentence structure while retaining the
original key concepts and semantic information. In order to alleviate the
hallucinations of LLMs, LLM-DA introduces a Text Faithfulness Filter (TFF) to
filter out unfaithful rewritten text. To balance the contributions of original
text and augmented text, a Balanced Sampling Strategy (BSS) is proposed to
control the proportion of original text and augmented text used for training.
LLM-DA is a plug-and-play method that can be easily integrated into various TPR
models. Comprehensive experiments on three TPR benchmarks show that LLM-DA can
improve the retrieval performance of current TPR models.

该论文提出了基于大型语言模型的数据增强方法（LLM-DA）以提升文本检索模型在人物图像检索中的性能。LLM-DA 使用大型语言模型重写当前的文本数据集，通过增加词汇和句子结构的多样性来扩展数据集，同时保持原始关键概念和语义信息。为了抑制大型语言模型的幻觉，LLM-DA 引入了文本忠实度过滤器（TFF）来筛除不忠实的重写文本。为了平衡原始文本和扩增文本的贡献，提出了一种平衡采样策略（BSS）。实验结果表明，LLM-DA 能够提升当前文本检索模型在人物图像检索任务上的性能。