Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query. Although numerous TIReID methods have been proposed and achieved promising performance, they implicitly assume the training image-text pairs are correctly aligned, which is not always the case in real-world scenarios. In practice, the image-text pairs inevitably exist under-correlated or even false-correlated, a.k.a noisy correspondence (NC), due to the low quality of the images and annotation errors. To address this problem, we propose a novel Robust Dual Embedding method (RDE) that can learn robust visual-semantic associations even with NC. Specifically, RDE consists of two main components: 1) A Confident Consensus Division (CCD) module that leverages the dual-grained decisions of dual embedding modules to obtain a consensus set of clean training data, which enables the model to learn correct and reliable visual-semantic associations. 2) A Triplet Alignment Loss (TAL) relaxes the conventional Triplet Ranking loss with the hardest negative samples to a log-exponential upper bound over all negative ones, thus preventing the model collapse under NC and can also focus on hard-negative samples for promising performance. We conduct extensive experiments on three public benchmarks, namely CUHK-PEDES, ICFG-PEDES, and RSTPReID, to evaluate the performance and robustness of our RDE. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on all three datasets. Code is available at https://github.com/QinYang79/RDE.

翻译：文本到图像行人重识别（TIReID）是跨模态领域中的一个引人注目的课题，旨在基于文本查询检索目标行人。尽管已有大量TIReID方法被提出并取得了显著性能，但它们隐含地假设训练图像-文本对是正确对齐的，而这在现实场景中并不总是成立。实际上，由于图像质量低下和标注错误，图像-文本对不可避免地存在欠相关甚至虚假相关，即所谓的噪声对应（NC）问题。为解决这一问题，我们提出了一种新颖的鲁棒双重嵌入方法（RDE），即使在NC情况下也能学习稳健的视觉-语义关联。具体而言，RDE包含两个主要组件：1）一种可信共识划分（CCD）模块，利用双重嵌入模块的双粒度决策获得干净训练数据的共识集，从而使模型能够学习正确且可靠的视觉-语义关联；2）一种三元组对齐损失（TAL），将传统以最困难负样本为基准的三元组排序损失放宽为所有负样本的对数指数上界，从而防止模型在NC下崩溃，同时还能关注困难负样本以实现优异性能。我们在三个公开基准数据集（即CUHK-PEDES、ICFG-PEDES和RSTPReID）上进行了大量实验，以评估RDE的性能和鲁棒性。我们的方法在三个数据集上，无论是否引入合成噪声对应，均取得了最先进的结果。代码可在https://github.com/QinYang79/RDE获取。