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Multi-modal entity alignment (MMEA) aims to discover identical entitiesacross different knowledge graphs (KGs) whose entities are associated withrelevant images. However, current MMEA algorithms rely on KG-level modalityfusion strategies for multi-modal entity representation, which ignores thevariations of modality preferences of different entities, thus compromisingrobustness against noise in modalities such as blurry images and relations.This paper introduces MEAformer, a multi-modal entity alignment transformerapproach for meta modality hybrid, which dynamically predicts the mutualcorrelation coefficients among modalities for more fine-grained entity-levelmodality fusion and alignment. Experimental results demonstrate that our modelnot only achieves SOTA performance in multiple training scenarios, includingsupervised, unsupervised, iterative, and low-resource settings, but also has alimited number of parameters, efficient runtime, and interpretability. Our codeis available at https://github.com/zjukg/MEAformer.

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid