arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.04726cs.CLcs.CV

编辑媒体理解框架:推理视觉虚假信息的意图与影响

Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation

Allen Institute for Artificial Intelligence(艾伦人工智能研究所);University of Michigan(密歇根大学);Paul G. Allen School of Computer Science & Engineering, University of Washington(华盛顿大学保罗·G·艾伦计算机科学与工程学院);Stanford University(斯坦福大学)

Jeff Da, Maxwell Forbes, Rowan Zellers, Anthony Zheng, Jena D. Hwang, Antoine Bosselut, Yejin Choi

AI总结:

本文提出编辑媒体理解任务及EMU数据集,并引入PELICAN模型,旨在识别图像编辑的意图与影响,实验显示模型准确率40.35%,仍有提升空间。

摘要:

多模态虚假信息,从“深度伪造”到简单的欺骗性编辑,是一个重要的社会问题。然而与此同时,绝大多数媒体编辑是无害的——例如一张经过滤镜处理的度假照片。这个例子与传播虚假信息的有害编辑之间的区别在于意图。识别并描述这种意图是当今人工智能系统面临的主要挑战。我们提出了编辑媒体理解任务,要求模型回答开放式问题,以捕捉图像编辑的意图和影响。我们为我们的任务引入了一个数据集EMU,包含48k个用丰富自然语言编写的问答对。我们评估了多种视觉与语言模型,并引入了一个新模型PELICAN,该模型基于预训练多模态表示的最新进展。我们的模型在我们的数据集上取得了有希望的结果,人类对其答案的准确率评分为40.35%。同时,仍有大量工作要做——人类更倾向于人类标注的标题,比例为93.56%——我们提供了分析,突出了进一步进展的领域。

英文摘要:

Multimodal disinformation, from 'deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- such as a filtered vacation photo. The difference between this example, and harmful edits that spread disinformation, is one of intent. Recognizing and describing this intent is a major challenge for today's AI systems. We present the task of Edited Media Understanding, requiring models to answer open-ended questions that capture the intent and implications of an image edit. We introduce a dataset for our task, EMU, with 48k question-answer pairs written in rich natural language. We evaluate a wide variety of vision-and-language models for our task, and introduce a new model PELICAN, which builds upon recent progress in pretrained multimodal representations. Our model obtains promising results on our dataset, with humans rating its answers as accurate 40.35% of the time. At the same time, there is still much work to be done -- humans prefer human-annotated captions 93.56% of the time -- and we provide analysis that highlights areas for further progress.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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