  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.
