Sometimes, human decision-making processes are not as rational as it seems. Each person has his/her own experience, knowledge, and preference on things and events surrounding us. This idiosyncratic behavior is hard to quantify and analyze and thus exists as an important obstacle to the complete understanding of human decision process and eventually implementation of more realistic artificial intelligence.
Understanding high-level concepts, e.g., interestingness, aesthetic beauty, attractiveness, atypicality, etc., and computational models to automatically estimate such concepts from complex, structured information are the essential first steps to reach the goal of understanding the irrational human behavior and decision making processes. To this end, we are investigating ways to measure and estimate these concepts from multimedia data.
Selected Related Publications
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A. Viola and S. Yoon, “A Hybrid Approach for Video Memorability Prediction,” in proceedings of MediaEval 2019 Workshop, Sophia Antipolis, France, October 27-29, 2019.
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A. Weiss, B. Sang, and S. Yoon, “Predicting Memorability via Early Fusion Deep Neural Network,” in proceedings of MediaEval 2018 Workshop, Sophia Antipolis, France, October 29-31, 2018.
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S. Yoon and J. Kim, “Object-centric Scene Understanding for Image Memorability Prediction,” The 1st IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), Miami, Florida, USA, 2018.
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S. Yoon and V. Pavlovic, “Sentiment Flow for Video Interestingness Prediction,” The 1st ACM Workshop on Human Centered Event Understanding from Multimedia (HuEvent) in conjunction with ACM International Conference on Multimedia (MM), Orlando, Florida, USA, 2014.
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J. Kim, S. Yoon, and V. Pavlovic, “Relative Spatial Features for Image Memorability,” The 21st ACM International Conference on Multimedia (MM), Barcelona, Cataluña, Spain, 2013.