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Found 5 papers, 2 papers with code
Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection
Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection.
Decoding the Underlying Meaning of Multimodal Hateful Memes
Recent studies have proposed models that yielded promising performance for the hateful meme classification task.
Prompting for Multimodal Hateful Meme Classification
Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pre-trained RoBERTa language model for hateful meme classification.
Ranked #3 on
Hateful Meme Classification
on HarMeme
On Explaining Multimodal Hateful Meme Detection Models
For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i. e., image and text) of the hateful memes.
Disentangling Hate in Online Memes
Our experiment results show that DisMultiHate is able to outperform state-of-the-art unimodal and multimodal baselines in the hateful meme classification task.
Ranked #4 on
Hateful Meme Classification
on HarMeme
