Text-based person re-identification (ReID) aims to identify images of the targeted person from a large-scale person image database according to a given textual description. However, due to significant inter-modal gaps, text-based person ReID remains a challenging problem. Most existing methods generally rely heavily on the similarity contributed by matched word-region pairs, while neglecting mismatched word-region pairs which may play a decisive role. Accordingly, we propose to mine false positive examples (MFPE) via a jointly optimized multi-branch architecture to handle this problem. MFPE contains three branches including a false positive mining (FPM) branch to highlight the role of mismatched word-region pairs. Besides, MFPE delicately designs a cross-relu loss to increase the gap of similarity scores between matched and mismatched word-region pairs. Extensive experiments on CUHK-PEDES demonstrate the superior effectiveness of MFPE. Our code is released at https://github.com/xx-adeline/MFPE.

翻译：文本描述的行人重识别（ReID）旨在根据给定的文本描述，从大规模行人图像数据库中识别目标人物的图像。然而，由于显著的模态间差异，基于文本的行人重识别仍然是一个具有挑战性的问题。现有的大多数方法通常严重依赖于匹配的词-区域对贡献的相似度，而忽略了可能起决定性作用的不匹配词-区域对。为此，我们提出通过一种联合优化的多分支架构来挖掘假阳性样本（MFPE），以处理这一问题。MFPE包含三个分支，其中一个假阳性挖掘（FPM）分支用于突出不匹配词-区域对的作用。此外，MFPE精心设计了一种交叉ReLU损失函数，以增大匹配与不匹配词-区域对之间的相似度分数差距。在CUHK-PEDES数据集上的大量实验证明了MFPE的卓越有效性。我们的代码已发布在https://github.com/xx-adeline/MFPE。