Automatic Understanding of Image and Video Advertisements
Abstract
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.
Cite
Text
Hussain et al. "Automatic Understanding of Image and Video Advertisements." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.123Markdown
[Hussain et al. "Automatic Understanding of Image and Video Advertisements." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/hussain2017cvpr-automatic/) doi:10.1109/CVPR.2017.123BibTeX
@inproceedings{hussain2017cvpr-automatic,
title = {{Automatic Understanding of Image and Video Advertisements}},
author = {Hussain, Zaeem and Zhang, Mingda and Zhang, Xiaozhong and Ye, Keren and Thomas, Christopher and Agha, Zuha and Ong, Nathan and Kovashka, Adriana},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2017},
doi = {10.1109/CVPR.2017.123},
url = {https://mlanthology.org/cvpr/2017/hussain2017cvpr-automatic/}
}