Weakly supervised text-based person re-identification (TPRe-ID) seeks to retrieve images of a target person using textual descriptions, without relying on identity annotations and is more challenging and practical. The primary challenge is the intra-class differences, encompassing intra-modal feature variations and cross-modal semantic gaps. Prior works have focused on instance-level samples and ignored prototypical features of each person which are intrinsic and invariant. Toward this, we propose a Cross-Modal Prototypical Contrastive Learning (CPCL) method. In practice, the CPCL introduces the CLIP model to weakly supervised TPRe-ID for the first time, mapping visual and textual instances into a shared latent space. Subsequently, the proposed Prototypical Multi-modal Memory (PMM) module captures associations between heterogeneous modalities of image-text pairs belonging to the same person through the Hybrid Cross-modal Matching (HCM) module in a many-to-many mapping fashion. Moreover, the Outlier Pseudo Label Mining (OPLM) module further distinguishes valuable outlier samples from each modality, enhancing the creation of more reliable clusters by mining implicit relationships between image-text pairs. Experimental results demonstrate that our proposed CPCL attains state-of-the-art performance on all three public datasets, with a significant improvement of 11.58%, 8.77% and 5.25% in Rank@1 accuracy on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively. The code is available at https://github.com/codeGallery24/CPCL.

翻译：弱监督文本行人重识别旨在通过文本描述检索目标人物图像，无需依赖身份标注，更具挑战性和实用性。其核心挑战在于类内差异，包括模态内特征差异和跨模态语义鸿沟。现有工作侧重于实例级样本，忽略了每个行人的内在不变原型特征。为此，本文提出跨模态原型对比学习方法。实践上，CPCL首次将CLIP模型引入弱监督文本行人重识别，将视觉和文本实例映射至共享隐空间。随后，所提出的原型多模态记忆模块通过混合跨模态匹配模块，以多对多映射方式捕获属于同一行人的图像-文本对中异质模态间的关联。此外，异常伪标签挖掘模块进一步从各模态中区分有价值的异常样本，通过挖掘图像-文本对间的隐式关系，增强更可靠聚类簇的生成。实验结果表明，所提出的CPCL在三个公开数据集上均取得了最优性能，在CUHK-PEDES、ICFG-PEDES和RSTPReid数据集上，Rank@1准确率分别显著提升11.58%、8.77%和5.25%。代码已开源至https://github.com/codeGallery24/CPCL。