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- W4387635670 abstract "Multimodal image-text memes are prevalent on the internet, serving as a unique form of communication that combines visual and textual elements to convey humor, ideas, or emotions. However, some memes take a malicious turn, promoting hateful content and perpetuating discrimination. Detecting hateful memes within this multimodal context is a challenging task that requires understanding the intertwined meaning of text and images. In this work, we address this issue by proposing a novel approach named ISSUES for multimodal hateful meme classification. ISSUES leverages a pre-trained CLIP vision-language model and the textual inversion technique to effectively capture the multimodal semantic content of the memes. The experiments show that our method achieves state-of-the-art results on the Hateful Memes Challenge and HarMeme datasets. The code and the pre-trained models are publicly available at https://github.com/miccunifi/ISSUES." @default.
- W4387635670 created "2023-10-14" @default.
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- W4387635670 date "2023-10-12" @default.
- W4387635670 modified "2023-10-15" @default.
- W4387635670 title "Mapping Memes to Words for Multimodal Hateful Meme Classification" @default.
- W4387635670 doi "https://doi.org/10.48550/arxiv.2310.08368" @default.
- W4387635670 hasPublicationYear "2023" @default.
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