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

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...

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Veröffentlicht in:	arXiv.org 2024-08
Hauptverfasser:	Su, Xuanyu, Li, Yansong, Inkpen, Diana, Japkowicz, Nathalie
Format:	Artikel
Sprache:	eng
Schlagworte:	Datasets Image enhancement Image segmentation Learning Parameter identification Parameter robustness Safety measures
Online-Zugang:	Volltext
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