Current image-text retrieval methods have demonstrated impressive performance
in recent years. However, they still face two problems: the inter-modal
matching missing problem and the intra-modal semantic loss problem. These
problems can significantly affect the accuracy of image-text retrieval. To
address these challenges, we propose a novel method called Cross-modal and
Uni-modal Soft-label Alignment (CUSA). Our method leverages the power of
uni-modal pre-trained models to provide soft-label supervision signals for the
image-text retrieval model. Additionally, we introduce two alignment
techniques, Cross-modal Soft-label Alignment (CSA) and Uni-modal Soft-label
Alignment (USA), to overcome false negatives and enhance similarity recognition
between uni-modal samples. Our method is designed to be plug-and-play, meaning
it can be easily applied to existing image-text retrieval models without
changing their original architectures. Extensive experiments on various
image-text retrieval models and datasets, we demonstrate that our method can
consistently improve the performance of image-text retrieval and achieve new
state-of-the-art results. Furthermore, our method can also boost the uni-modal
retrieval performance of image-text retrieval models, enabling it to achieve
universal retrieval. The code and supplementary files can be found at
this https URL

通过引入交叉模态和单模态软标签对齐 (CUSA) 方法，我们解决了图像 - 文本检索中的两个问题：模态间匹配缺失和模态内语义损失。实验证明，我们的方法可以提升图像 - 文本检索以及单模态检索的性能，达到新的最先进水平。