Leveraging Audio Gestalt to Predict Media Memorability
December 31, 2020 Β· Declared Dead Β· π MediaEval Benchmarking Initiative for Multimedia Evaluation
"No code URL or promise found in abstract"
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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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