  Multi-Modal Entity Alignment aims to discover identical entities across
heterogeneous knowledge graphs. While recent studies have delved into fusion
paradigms to represent entities holistically, the elimination of features
irrelevant to alignment and modal inconsistencies is overlooked, which are
caused by inherent differences in multi-modal features. To address these
challenges, we propose a novel strategy of progressive modality freezing,
called PMF, that focuses on alignmentrelevant features and enhances multi-modal
feature fusion. Notably, our approach introduces a pioneering cross-modal
association loss to foster modal consistency. Empirical evaluations across nine
datasets confirm PMF's superiority, demonstrating stateof-the-art performance
and the rationale for freezing modalities. Our code is available at
https://github.com/ninibymilk/PMF-MMEA.
