With the rapid development of information technology, cross-modal data alignment and retrieval is one of the hotspots in multimodal learning. The aim is to reduce the representation differences among different modalities and enable different representations to be applicable to data of the same modality. Cross-modal data retrieval requires based on this alignment technology to retrieve data of other modalities corresponding to a certain modality from a certain modality. This paper briefly introduces the problems and challenges of cross-modal data alignment and retrieval, and mentions the application of multimodal learning methods. It proposes many new schemes for cross-modal data alignment and retrieval, such as constructing a unified embedding space, integrating semantic perception mechanisms, information enhancement and redundancy suppression, and adopting self-supervised and transfer mechanisms and other technologies. Through case studies, the effectiveness and feasibility of these schemes are demonstrated, and the multimodal learning methods are comprehensively summarized, and the development direction is prospected.

Bukun Ren. Multimodal Learning Method for Cross-Modal Data Alignment and Retrieval. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 1-8. https://doi.org/10.38007/IJMC.2026.070101.

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