Multimodal out-of-context news is a common type of misinformation on online
media platforms. This involves posting a caption, alongside an invalid
out-of-context news image. Reflecting its importance, researchers have
developed models to detect such misinformation. However, a common limitation of
these models is that they only consider the scenario where pre-labeled data is
available for each domain, failing to address the out-of-context news detection
on unlabeled domains (e.g., unverified news on new topics or agencies). In this
work, we therefore focus on domain adaptive out-of-context news detection. In
order to effectively adapt the detection model to unlabeled news topics or
agencies, we propose ConDA-TTA (Contrastive Domain Adaptation with Test-Time
Adaptation) which applies contrastive learning and maximum mean discrepancy
(MMD) to learn the domain-invariant feature. In addition, it leverages target
domain statistics during test-time to further assist domain adaptation.
Experimental results show that our approach outperforms baselines in 5 out of 7
domain adaptation settings on two public datasets, by as much as 2.93% in F1
and 2.08% in accuracy.

提出了一种基于对比学习和最大均值差异的测试时间自适应的 Contrastive Domain Adaptation with Test-Time Adaptation (ConDA-TTA) 方法，用于多模态脱离上下文新闻检测问题，实验证明该方法在 7 个领域自适应设置中的 5 个上胜过基线方法，F1 指标有 2.93% 的提升，准确性提高了 2.08%。