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Hateful Meme Classification

Hateful meme classification aims to detect harmful content within the text or images of memes.

Papers

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Showing 1–17 of 17 papers

TitleStatusHype
LLM-based Semantic Augmentation for Harmful Content Detection—0
Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme DetectionCode1
Demystifying Hateful Content: Leveraging Large Multimodal Models for Hateful Meme Detection with Explainable Decisions—0
Prompt-enhanced Network for Hateful Meme ClassificationCode0
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme ClassificationCode1
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language ModelsCode1
Improving Hateful Meme Detection through Retrieval-Guided Contrastive LearningCode1
Mapping Memes to Words for Multimodal Hateful Meme ClassificationCode1
Decoding the Underlying Meaning of Multimodal Hateful MemesCode1
Prompting for Multimodal Hateful Meme Classification—0
Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP FeaturesCode1
0/1 Deep Neural Networks via Block Coordinate Descent—0
On Explaining Multimodal Hateful Meme Detection Models—0
Detecting Harmful Memes and Their Targets—0
Disentangling Hate in Online Memes—0
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes—0
Learning Transferable Visual Models From Natural Language SupervisionCode2
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