2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2016
DOI: 10.1109/cvprw.2016.102
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Inferring Visual Persuasion via Body Language, Setting, and Deep Features

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Cited by 28 publications
(15 citation statements)
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“…There are diverse ways of characterizing the contributions of text and imagery. Gao et al (2015) investigate the genre of image captions and Huang and Kovashka (2016) study the persuasive implicit relationships between text and images. Kruk et al (2019b) study the emotional links between text and images.…”
Section: Related Workmentioning
confidence: 99%
“…There are diverse ways of characterizing the contributions of text and imagery. Gao et al (2015) investigate the genre of image captions and Huang and Kovashka (2016) study the persuasive implicit relationships between text and images. Kruk et al (2019b) study the emotional links between text and images.…”
Section: Related Workmentioning
confidence: 99%
“…For example, high levels of blinking during a high-anxiety-inducing task (such as an exam) have a different meaning than in a low anxiety-inducing task (such as playing a non-competitive game). Thus, an over-reliance on micro-level cues might obscure the broader context in which those NVBs are produced and misconstrue their communicative intents (Huang & Kovashka, 2016). Psychology offers comprehensive models to record NVB within the micro-, meso-and macro-levels.…”
Section: Current Challenges For Personality Computingmentioning
confidence: 99%
“…Political ideology and communicative intents have also been studied in computer vision. Political images have been analyzed to infer the persuasive intents using various features such as facial display types, body poses, and scene context (Joo et al, 2014;Huang and Kovashka, 2016;Joo and Steinert-Threlkeld, 2018;Bai et al, 2020;. Joo et al (2015) introduce a method that infers the perceived characteristics of politicians using face images and show that those characteristics can be used in elections forecasting.…”
Section: Political Ideology Predictionmentioning
confidence: 99%