Kavli Affiliate: Wei Gao
| First 5 Authors: Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang,
| Summary:
Detecting multimodal misinformation, especially in the form of image-text
pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking
datasets for training detectors is costly, leading researchers to use synthetic
datasets generated by AI technologies. However, the generalizability of
detectors trained on synthetic data to real-world scenarios remains unclear due
to the distribution gap. To address this, we propose learning from synthetic
data for detecting real-world multimodal misinformation through two
model-agnostic data selection methods that match synthetic and real-world data
distributions. Experiments show that our method enhances the performance of a
small MLLM (13B) on real-world fact-checking datasets, enabling it to even
surpass GPT-4V~cite{GPT-4V}.
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