@inproceedings{su-etal-2025-context,
    title = "A Context-Aware Contrastive Learning Framework for Hateful Meme Detection and Segmentation",
    author = "Su, Xuanyu  and
      Li, Yansong  and
      Inkpen, Diana  and
      Japkowicz, Nathalie",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.289/",
    pages = "5201--5215",
    ISBN = "979-8-89176-195-7",
    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 HateSieve, a new framework designed to enhance the detection and segmentation of hateful elements in memes. HateSieve features a novel Contrastive Meme Generator that creates semantically correlated 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 show that 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. Caution: Contains academic discussions of hate speech; viewer discretion advised."
}