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-图像行人重识别（TIReID）是跨模态领域一个令人关注的研究课题，旨在基于文本查询检索目标行人。尽管已有众多TIReID方法被提出并取得了显著性能，但它们通常隐式假设训练中的图像-文本对是正确对齐的，然而在实际场景中这一假设往往不成立。实践中，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误相关的情况，即噪声对应（NC）。为解决此问题，我们提出了一种新颖的鲁棒双嵌入方法（RDE），即使在存在NC的情况下也能学习到鲁棒的视觉-语义关联。具体而言，RDE包含两个核心组件：1）自信共识划分（CCD）模块，通过利用双嵌入模块的双粒度决策获取干净训练数据的共识集合，从而使模型能够学习正确且可靠的视觉-语义关联。2）三元组对齐损失（TAL）将传统使用最困难负样本的三元组排序损失松弛为所有负样本的对数指数上界，从而在NC条件下防止模型崩溃，同时仍能聚焦于困难负样本以获得优异性能。我们在三个公开基准数据集——CUHK-PEDES、ICFG-PEDES和RSTPReID上开展了大量实验，以评估RDE的性能与鲁棒性。我们的方法在三个数据集上（无论是否包含合成噪声对应）均达到了最先进水平。代码已开源：https://github.com/QinYang79/RDE。