2018/08/14 by Abhinav Shukla, Harish Katti, Mohan Kankanhalli +1
Computer Science · Neuroscience · Psychology · #Affect (linguistics) #Artificial intelligence #Cognitive psychology #Communication #Computer science #ENCODE #Emotion and Mood Recognition #Face Recognition and Perception #Feature (linguistics) #Gaze #Key (lock) #Linguistics #Narrative #Object (grammar) #Psychology #Recall #Salient #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.1145/3242969.3242988
Accepted for publication in the Proceedings of 20th ACM International Conference on Multimodal Interaction, Boulder, CO, USA
arxiv created 2018/08/14 · arxiv updated 2018/08/15 · openalex publication_date 2018/10/02 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/29
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.