  Modeling what makes an advertisement persuasive, i.e., eliciting the desired
response from consumer, is critical to the study of propaganda, social
psychology, and marketing. Despite its importance, computational modeling of
persuasion in computer vision is still in its infancy, primarily due to the
lack of benchmark datasets that can provide persuasion-strategy labels
associated with ads. Motivated by persuasion literature in social psychology
and marketing, we introduce an extensive vocabulary of persuasion strategies
and build the first ad image corpus annotated with persuasion strategies. We
then formulate the task of persuasion strategy prediction with multi-modal
learning, where we design a multi-task attention fusion model that can leverage
other ad-understanding tasks to predict persuasion strategies. Further, we
conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500
companies where we use our model's predictions to analyze which strategies work
with different demographics (age and gender). The dataset also provides image
segmentation masks, which labels persuasion strategies in the corresponding ad
images on the test split. We publicly release our code and dataset
https://midas-research.github.io/persuasion-advertisements/.
