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There is more to images than their objective physical content: for example, advertisements are created to persuade a
viewer to take a certain action. We propose the novel problem of automatic advertisement understanding. To enable
research on this problem, we create two datasets: an image dataset of 64,832 image ads, and a video dataset of 3,477
ads. Our data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers
describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer
("What should I do according to this ad, and why should I do it?"), and symbolic references ads make (e.g. a dove
symbolizes peace). We also analyze the most common persuasive strategies ads use, and the capabilities that computer
vision systems should have to understand these strategies. We present baseline classification results for several
prediction tasks, including automatically answering questions about the messages of the ads.

Marketing communications are the primary means of connecting brand with consumers through which the consumer can know
what the product is about, what it stands for, who makes it and can be motivated to try it out.Advertisements use
various persuasion strategies which allows marketers to link their brands with people, places, experiences, feelings,
events, and other things. Through literature survey and analyzing several advertisments, we finalized that persuasion
can be
carried out using 24 strategies which includes reciprocity, scarcity, concreteness, authority approval, etc. For this
problem, we used the Kovashka's dataset of 64,832 ad images and further added the persuasive strategies labels. Dataset
was partially annotated by human annotators, and we used active learning to get persuasive strategies for the rest of
the
images.