# CL-DMDF:Dynamic Multimodal Data Fusion Model Based on Contrastive Learning Source: [CL-DMDF:Dynamic Multimodal Data Fusion Model Based on Contrastive Learning]() Collection index: [llms.txt]() Data updated: 2026-06-04T05:00:05.280Z Authors: Dong Li, Lingling Zhang, Binghao Han, Linlin Ding, Yue Kou Published: 2026-06-01T07:12:17.000Z ## Abstract 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. Some traditional models overly emphasize local interactions within missing modalities, neglecting the global complementary cues embedded in multimodal representations. To overcome these limitations, we propose a Dynamic Multimodal Data Fusion model based on Contrastive Learning (CL-DMDF). CL-DMDF introduces a novel attention mechanism that operates across both feature and modality dimensions to compute reliable attention scores, effectively reflecting importance at each level. The CL-DMDF further incorporates an entity-centroid contrastive learning module that constructs centroid-based positive samples from entity features to enhance discriminative learning. Additionally, an adaptive fusion module is employed to improve the efficiency and accuracy of dynamic fusion strategies. Extensive experiments conducted on three datasets demonstrate the effectiveness of the CL-DMDF across diverse multimodal fusion tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multimodal Sentiment Analysis on CMU-MOSEI](): Multimodal Sentiment Analysis; CMU-MOSEI; MAE: 0.737. - [Multimodal Multilabel Classification on MM-IMDB (test)](): Multimodal Multilabel Classification; MM-IMDB (test); Macro F1: 53.28. - [Semantic Segmentation on NYU Depth V2](): Semantic segmentation; NYU Depth V2; mIoU: 52.3. ## Original sources and implementation [Original paper]() [PDF]()