AI RESEARCH
CL-DMDF:Dynamic Multimodal Data Fusion Model Based on Contrastive Learning
arXiv CS.LG
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ArXi:2606.02659v1 Announce Type: new Multimodal data fusion involves integrating and analyzing information from multiple modalities to uncover latent correlations and complementary patterns, thereby enhancing data processing and decision-making. While existing methods for structured multimodal inputs are typically designed around specific tasks and assume fully observed modalities, real-world applications often suffer from uncertain or missing modality inputs due to various factors.