@Article{cmc.2026.085456,
AUTHOR = {Huayu Li, Xiang Wang, Jia Luo, Xiaotong He, Peiying Zhang},
TITLE = {A Two-Stage Decoupled Matching Network for Multimodal Entity Linking},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27684},
ISSN = {1546-2226},
ABSTRACT = {Multimodal Entity Linking (MEL) aims to map ambiguous mentions in multimodal contexts to their corresponding entities in a multimodal knowledge base. However, existing methods still face limitations in terms of feature extraction granularity, the depth of cross-modal interaction, and architectural coupling. To address these issues, we propose a Two-stage Decoupled Matching Network (TDMN) for multimodal entity linking. The matching process is divided into two stages: intra-modal matching and cross-modal interaction. In the intra-modal stage, textual and visual inputs are processed independently. The framework then proceeds to the cross-modal interaction stage, following the principle of “enhancement prior to interaction.” Specifically, unimodal features are first refined through a parallel dual-attention network consisting of Global Relational Attention and Adaptive Sharpening Attention, together with a multi-granularity calibration fusion module. Based on the refined representations, cross-modal alignment is subsequently performed within a symmetric bidirectional interaction architecture, in which a gated residual mechanism is introduced to facilitate information fusion. Experiments conducted on the public benchmark datasets WikiMEL and WikiDiverse demonstrate the effectiveness of TDMN. Compared with the <mml:math id="mml-ieqn-1"><mml:msup><mml:mtext>M</mml:mtext><mml:mn>3</mml:mn></mml:msup></mml:math>EL baseline, TDMN achieves absolute improvements of 1.39% and 1.88% in MRR and Hits@1, respectively, on the WikiDiverse dataset. In addition, compared with MIMIC, TDMN improves MRR and Hits@1 by 0.8% and 1.21%, respectively, on the WikiMEL dataset. These results support the effectiveness of the proposed approach.},
DOI = {10.32604/cmc.2026.085456}
}