  Memes are one of the most popular types of content used to spread information
online. They can influence a large number of people through rhetorical and
psychological techniques. The task, Detection of Persuasion Techniques in Texts
and Images, is to detect these persuasive techniques in memes. It consists of
three subtasks: (A) Multi-label classification using textual content, (B)
Multi-label classification and span identification using textual content, and
(C) Multi-label classification using visual and textual content. In this paper,
we propose a transfer learning approach to fine-tune BERT-based models in
different modalities. We also explore the effectiveness of ensembles of models
trained in different modalities. We achieve an F1-score of 57.0, 48.2, and 52.1
in the corresponding subtasks.
