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Memes are prevalent on the internet and continue to grow and evolve alongsideour culture. An automatic understanding of memes propagating on the internetcan shed light on the general sentiment and cultural attitudes of people. Inthis work, we present team BLUE's solution for the second edition of theMEMOTION shared task. We showcase two approaches for meme classification (i.e.sentiment, humour, offensive, sarcasm and motivation levels) using a text-onlymethod using BERT, and a Multi-Modal-Multi-Task transformer network thatoperates on both the meme image and its caption to output the final scores. Inboth approaches, we leverage state-of-the-art pretrained models for text (BERT,Sentence Transformer) and image processing (EfficientNetV4, CLIP). Through ourefforts, we obtain first place in task A, second place in task B and thirdplace in task C. In addition, our team obtained the highest average score forall three tasks.

BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer