Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval


  • Hailang Huang Beihang University
  • Zhijie Nie Beihang University
  • Ziqiao Wang University of Ottawa
  • Ziyu Shang Southeast University



NLP: Language Grounding & Multi-modal NLP, CV: Language and Vision


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



How to Cite

Huang, H., Nie, Z., Wang, Z., & Shang, Z. (2024). Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 18298-18306.



AAAI Technical Track on Natural Language Processing I