Leveraging Audio Gestalt to Predict Media Memorability

December 31, 2020 Β· Declared Dead Β· πŸ› MediaEval Benchmarking Initiative for Multimedia Evaluation

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Authors Lorin Sweeney, Graham Healy, Alan F. Smeaton arXiv ID 2012.15635 Category cs.MM: Multimedia Cross-listed cs.AI, cs.CV Citations 6 Venue MediaEval Benchmarking Initiative for Multimedia Evaluation Last Checked 3 months ago
Abstract
Memorability determines what evanesces into emptiness, and what worms its way into the deepest furrows of our minds. It is the key to curating more meaningful media content as we wade through daily digital torrents. The Predicting Media Memorability task in MediaEval 2020 aims to address the question of media memorability by setting the task of automatically predicting video memorability. Our approach is a multimodal deep learning-based late fusion that combines visual, semantic, and auditory features. We used audio gestalt to estimate the influence of the audio modality on overall video memorability, and accordingly inform which combination of features would best predict a given video's memorability scores.
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