The study explores the effectiveness of Large Vision-Language Models (LVLMs) in detecting out-of-context (OOC) information, specifically focusing on images and texts. It highlights the challenges of OOC detection and the potential of LVLMs in addressing this issue. The research demonstrates that LVLMs require fine-tuning on multimodal OOC datasets to enhance their accuracy. By fine-tuning MiniGPT-4 on the NewsCLIPpings dataset, significant improvements in OOC detection accuracy were observed. The study emphasizes the importance of adapting LVLMs to specific tasks for improved performance in detecting anomalies between images and texts.
The paper discusses the growing threat of misinformation, particularly through multimodal means combining images with text to deceive or mislead individuals. Various terms related to misinformation are identified, such as rumor, fake news, and disinformation. The authors illustrate examples of multimodal OOC content where authentic images are paired with misleading captions to alter the original message intentionally.
Furthermore, the research delves into related work on vision-language models like CLIP and VisualBERT, showcasing advancements in pre-training visual-language representations for enhanced comprehension of visual and textual information by machines. The study also presents a novel methodology for detecting OOC content using synthetic data generation with large pre-trained VLMs.
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by Fatma Shalab... om arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.08776.pdfDiepere vragen