Multi-modal Ensemble Models for Predicting Video Memorability

• URL: http://arxiv.org/abs/2102.01173v1
• Date: Mon, 1 Feb 2021 21:16:52 GMT
• Title: Multi-modal Ensemble Models for Predicting Video Memorability
• Authors: Tony Zhao, Irving Fang, Jeffrey Kim, Gerald Friedland
• Abstract summary: This work introduces and demonstrates the efficacy and high generalizability of extracted audio embeddings as a feature for the task of predicting media memorability.
• Score: 3.8367329188121824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling media memorability has been a consistent challenge in the field of machine learning. The Predicting Media Memorability task in MediaEval2020 is the latest benchmark among similar challenges addressing this topic. Building upon techniques developed in previous iterations of the challenge, we developed ensemble methods with the use of extracted video, image, text, and audio features. Critically, in this work we introduce and demonstrate the efficacy and high generalizability of extracted audio embeddings as a feature for the task of predicting media memorability.

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