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