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Affect Recognition in Ads with Application to Computational AdvertisingAuthors: abhinav.shukla ,Shruti Shriya Gullapuram,Harish Katti,Karthik Yadati,Mohan Kankanhalli,Ramanathan Subramanian Conference: 25th ACM International Conference on Multimedia (ACMMM-2017 2017) Location Mountain View, CA USA Date: 2017-10-23 Report no: IIIT/TR/2017/56 AbstractAdvertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors [9] upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application. Full paper: pdf Centre for Visual Information Technology |
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