Leveraging Audio Gestalt to Predict Media Memorability
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). First posted on arXiv 31 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2021The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability Systemcited by 5
- 2022Diffusing Surrogate Dreams of Video Scenes to Predict Video Memorabilitycited by 4
- 2021Predicting Media Memorability: Comparing Visual, Textual and Auditory Featurescited by 3
- 2023Using Saliency and Cropping to Improve Video Memorabilitycited by 0
- 2024Generative Outpainting To Enhance the Memorability of Short-Form Videoscited by 0
- 2024Generative Outpainting To Enhance the Memorability of Short-Form Videos21 Nov 2024
- 2023Using Saliency and Cropping to Improve Video Memorability21 Sep 2023
- 2022Diffusing Surrogate Dreams of Video Scenes to Predict Video Memorability19 Dec 2022
- 2021Predicting Media Memorability: Comparing Visual, Textual and Auditory Features15 Dec 2021
- 2021The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability System23 Apr 2021