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 hardest negatives, which tends to rapidly overfit NC,
to a log-exponential upper bound over all negatives, thus preventing the model
from overemphasizing false image-text pairs. 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.

提出了一种用于文本到图像的人物再识别的鲁棒双重嵌入方法 (RDE)，能够学习到在存在噪音对应的情况下的可靠的视觉语义关联。