Apr 1, 2018
Understanding Effective and Emotional Components in Advertisements
DenseNet-121 based modeling of arousal, valence, and ad effectiveness with CVPR workshop acceptance.
Overview
Defined emotional components of ads using arousal and valence signals, then modeled ad effectiveness through learned visual predictors.
What I built
- Emotion score mapping pipeline based on sentiment-tagged corpus.
- Effective score proxy using user-response heuristics.
- Fine-tuned DenseNet-121 models for arousal, valence, and effectiveness prediction.
Results
Accepted as a poster at the CVPR Understanding Visual Advertisements Workshop (Salt Lake City, 2018).