通过图像标题监督的多模态仇恨模因检测

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Huaicheng Fang, Fuqing Zhu, Jizhong Han, Songlin Hu
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引用次数: 0

摘要

大量的仇恨言论以社交媒体用户上传的文字和图片的形式存在于互联网上。近年来,多模态仇恨语音检测任务吸引了越来越多的研究者的投入,并产生了一些具有代表性的负样本感知工作。对于这种特殊的多模态任务,多模态语义信息理解能力尤为重要。然而,由于每幅图像的外观复杂性,现有模型对图像模态语义的理解能力与文本模态相比不足。因此,本文利用该模型所能理解的文本情态来提高对图像情态语义的理解能力。具体而言,本文提出了一种用于多模态仇恨语音检测的图像标题监督(ICS)辅助方法,其中图像标题被设计为监督图像的特征学习,以进一步理解语义信息。在Facebook仇恨模因数据集上,所提出的ICS方法优于一些最先进的单模态和多模态基线,证明了ICS的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Hateful Memes Detection via Image Caption Supervision
A large amount of hateful speech exist on the Internet in the form of text and images uploaded by social media users. Recently, multimodal hateful speech detection task has attracted more and more researchers to invest, producing some representative work for perceiving the negative samples. For this special multimodal task, the ability of multimodal semantic information understanding is particularly crucial. However, the existing models have insufficient understanding ability of image modality semantic compared with the text modality, due to the appearance complexity of each image. Therefore, this paper utilizes the text modality which is well understood by the model to improve understanding ability of image modality semantic. Specifically, this paper proposes an image caption supervision (ICS) auxiliary method for multimodal hateful speech detection, where the image caption is designed to supervise the feature learning of images for further understanding the semantic information. On the Facebook Hateful Memes dataset, the proposed ICS method outperforms some state-of-the-art unimodal and multimodal baselines, demonstrating the effectiveness of ICS.
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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