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.
@article{arxiv.2407.16168,
title = {Progressively Modality Freezing for Multi-Modal Entity Alignment},
author = {Yani Huang and Xuefeng Zhang and Richong Zhang and Junfan Chen and Jaein Kim},
journal= {arXiv preprint arXiv:2407.16168},
year = {2024}
}