  Multi-modal entity alignment (MMEA) aims to discover identical entities
across different knowledge graphs (KGs) whose entities are associated with
relevant images. However, current MMEA algorithms rely on KG-level modality
fusion strategies for multi-modal entity representation, which ignores the
variations of modality preferences of different entities, thus compromising
robustness against noise in modalities such as blurry images and relations.
This paper introduces MEAformer, a multi-modal entity alignment transformer
approach for meta modality hybrid, which dynamically predicts the mutual
correlation coefficients among modalities for more fine-grained entity-level
modality fusion and alignment. Experimental results demonstrate that our model
not only achieves SOTA performance in multiple training scenarios, including
supervised, unsupervised, iterative, and low-resource settings, but also has a
limited number of parameters, efficient runtime, and interpretability. Our code
is available at https://github.com/zjukg/MEAformer.
