Text-to-image person re-identification (TIReID) aims to retrieve the target person from an image gallery via a textual description query. Recently, pre-trained vision-language models like CLIP have attracted significant attention and have been widely utilized for this task due to their robust capacity for semantic concept learning and rich multi-modal knowledge. However, recent CLIP-based TIReID methods commonly rely on direct fine-tuning of the entire network to adapt the CLIP model for the TIReID task. Although these methods show competitive performance on this topic, they are suboptimal as they necessitate simultaneous domain adaptation and task adaptation. To address this issue, we attempt to decouple these two processes during the training stage. Specifically, we introduce the prompt tuning strategy to enable domain adaptation and propose a two-stage training approach to disentangle domain adaptation from task adaptation. In the first stage, we freeze the two encoders from CLIP and solely focus on optimizing the prompts to alleviate domain gap between the original training data of CLIP and downstream tasks. In the second stage, we maintain the fixed prompts and fine-tune the CLIP model to prioritize capturing fine-grained information, which is more suitable for TIReID task. Finally, we evaluate the effectiveness of our method on three widely used datasets. Compared to the directly fine-tuned approach, our method achieves significant improvements.

翻译：文本到图像行人重识别（TIReID）旨在通过文本描述查询从图像库中检索目标行人。近年来，预训练的视觉-语言模型（如CLIP）因其强大的语义概念学习能力和丰富的多模态知识而受到广泛关注，并被普遍应用于该任务。然而，当前基于CLIP的TIReID方法通常依赖对整个网络直接微调，以将CLIP模型适配至TIReID任务。尽管这些方法在该问题上展现了竞争性性能，但由于需要同时进行领域适配和任务适配，它们并非最优解。为解决此问题，我们尝试在训练阶段解耦这两个过程。具体而言，我们引入提示调优策略实现领域适配，并提出一种两阶段训练方法以将领域适配与任务适配相分离。第一阶段中，我们冻结CLIP的两个编码器，仅专注于优化提示以缓解CLIP原始训练数据与下游任务之间的领域差距。第二阶段中，我们保持固定提示并对CLIP模型进行微调，使其优先捕捉更适合TIReID任务的细粒度信息。最后，我们在三个广泛使用的数据集上评估了所提方法的有效性。与直接微调方法相比，我们的方法取得了显著改进。