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Multimodal image-text memes are prevalent on the internet, serving as aunique form of communication that combines visual and textual elements toconvey humor, ideas, or emotions. However, some memes take a malicious turn,promoting hateful content and perpetuating discrimination. Detecting hatefulmemes within this multimodal context is a challenging task that requiresunderstanding the intertwined meaning of text and images. In this work, weaddress this issue by proposing a novel approach named ISSUES for multimodalhateful meme classification. ISSUES leverages a pre-trained CLIPvision-language model and the textual inversion technique to effectivelycapture the multimodal semantic content of the memes. The experiments show thatour method achieves state-of-the-art results on the Hateful Memes Challenge andHarMeme datasets. The code and the pre-trained models are publicly available athttps://github.com/miccunifi/ISSUES.

Mapping Memes to Words for Multimodal Hateful Meme Classification