Abstract:
Advertisements (ads) often contain strong emotions to capture audience attention and convey an effective message. Still, little work
has focused on affect recognition (AR) from ads employing audiovisual
or user cues. This work (1) compiles an affective video ad dataset which
evokes coherent emotions across users; (2) explores the efficacy of
content-centric convolutional neural network (CNN) features for ad AR
vis-a-vis handcrafted audio-visual descriptors; (3) examines user-centric ˜
ad AR from Electroencephalogram (EEG) signals, and (4) demonstrates
how better affect predictions facilitate effective computational advertising
via a study involving 18 users. Experiments reveal that (a) CNN features
outperform handcrafted audiovisual descriptors for content-centric AR;
(b) EEG features encode ad-induced emotions better than contentbased features; (c) Multi-task learning achieves optimal ad AR among
a slew of classifiers and (d) Pursuant to (b), EEG features enable
optimized ad insertion onto streamed video compared to content-based
or manual insertion, maximizing ad recall and viewing experience.
