27 research outputs found
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Emotion evoked by an advertisement plays a key role in influencing brand
recall and eventual consumer choices. Automatic ad affect recognition has
several useful applications. However, the use of content-based feature
representations does not give insights into how affect is modulated by aspects
such as the ad scene setting, salient object attributes and their interactions.
Neither do such approaches inform us on how humans prioritize visual
information for ad understanding. Our work addresses these lacunae by
decomposing video content into detected objects, coarse scene structure, object
statistics and actively attended objects identified via eye-gaze. We measure
the importance of each of these information channels by systematically
incorporating related information into ad affect prediction models. Contrary to
the popular notion that ad affect hinges on the narrative and the clever use of
linguistic and social cues, we find that actively attended objects and the
coarse scene structure better encode affective information as compared to
individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International
Conference on Multimodal Interaction, Boulder, CO, US
Human Visual Perception, study and applications to understanding Images and Videos
Ph.DDOCTOR OF PHILOSOPH
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
Affect Recognition in Ads with Application to Computational Advertising
Advertisements (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 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.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM)
201
Affective Computational Advertising Based on Perceptual Metrics
We present \textbf{ACAD}, an \textbf{a}ffective \textbf{c}omputational
\textbf{ad}vertising framework expressly derived from perceptual metrics.
Different from advertising methods which either ignore the emotional nature of
(most) programs and ads, or are based on axiomatic rules, the ACAD formulation
incorporates findings from a user study examining the effect of within-program
ad placements on ad perception. A linear program formulation seeking to achieve
(a) \emph{{genuine}} ad assessments and (b) \emph{maximal} ad recall is then
proposed. Effectiveness of the ACAD framework is confirmed via a validational
user study, where ACAD-induced ad placements are found to be optimal with
respect to objectives (a) and (b) against competing approaches.Comment: 13 pages, 13 figure