HateSieve: A Contrastive Learning Framework for Detecting and Segmenting Hateful Content in Multimodal Memes

• URL: http://arxiv.org/abs/2408.05794v1
• Date: Sun, 11 Aug 2024 14:56:06 GMT
• Title: HateSieve: A Contrastive Learning Framework for Detecting and Segmenting Hateful Content in Multimodal Memes
• Authors: Xuanyu Su, Yansong Li, Diana Inkpen, Nathalie Japkowicz,
• Abstract summary: textscHateSieve is a framework designed to enhance the detection and segmentation of hateful elements in memes. textscHateSieve features a novel Contrastive Meme Generator that creates semantically paired memes. Empirical experiments on the Hateful Meme show that textscHateSieve not only surpasses existing LMMs in performance with fewer trainable parameters but also offers a robust mechanism for precisely identifying and isolating hateful content.
• Score: 8.97062933976566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Amidst the rise of Large Multimodal Models (LMMs) and their widespread application in generating and interpreting complex content, the risk of propagating biased and harmful memes remains significant. Current safety measures often fail to detect subtly integrated hateful content within ``Confounder Memes''. To address this, we introduce \textsc{HateSieve}, a new framework designed to enhance the detection and segmentation of hateful elements in memes. \textsc{HateSieve} features a novel Contrastive Meme Generator that creates semantically paired memes, a customized triplet dataset for contrastive learning, and an Image-Text Alignment module that produces context-aware embeddings for accurate meme segmentation. Empirical experiments on the Hateful Meme Dataset show that \textsc{HateSieve} not only surpasses existing LMMs in performance with fewer trainable parameters but also offers a robust mechanism for precisely identifying and isolating hateful content. \textcolor{red}{Caution: Contains academic discussions of hate speech; viewer discretion advised.}

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