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Memes on the Internet are often harmless and sometimes amusing. However, byusing certain types of images, text, or combinations of both, the seeminglyharmless meme becomes a multimodal type of hate speech -- a hateful meme. TheHateful Memes Challenge is a first-of-its-kind competition which focuses ondetecting hate speech in multimodal memes and it proposes a new data setcontaining 10,000+ new examples of multimodal content. We utilize VisualBERT --which meant to be the BERT of vision and language -- that was trainedmultimodally on images and captions and apply Ensemble Learning. Our approachachieves 0.811 AUROC with an accuracy of 0.765 on the challenge test set andplaced third out of 3,173 participants in the Hateful Memes Challenge.

Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge