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arXiv Computation and Language By Kai Qian, Dongjie Xu, Weijie Shi, Yezhao Xu, Jiaqi Wang, Hanghui Guo, Mengze Li, Hao Chen, Ziyi Liu, Jiajie Xu

Decoupling and Regulating Modality Heads for Fake News Detection under Missing Modality

Read the original on arXiv Computation and Language →

The paper introduces Head-wise Modality Decoupling and Regulation (HMDR) to improve fake news detection when some modalities (image or text) are missing. HMDR first decouples modality representations via soft head grouping based on attention preference, then regulates head contributions to balance modality attention, and finally enforces modality invariance by aligning representations when one modality is replaced. Experiments on two benchmarks demonstrate that HMDR enhances detection performance under missing modalities while preserving accuracy on complete inputs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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