# Predicting Media Memorability: Comparing Visual, Textual and Auditory Features

arXiv: https://arxiv.org/abs/2112.07969 · HTML: https://mlsanity.com/paper/2112.07969/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability](https://mlsanity.com/paper/2009.02568/) (2020)
- [Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?](https://mlsanity.com/paper/2012.15650/) (2020)
- [Leveraging Audio Gestalt to Predict Media Memorability](https://mlsanity.com/paper/2012.15635/) (2020)
- [The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability System](https://mlsanity.com/paper/2104.11568/) (2021)
- [Overview of The MediaEval 2021 Predicting Media Memorability Task](https://mlsanity.com/paper/2112.05982/) (2021)
- [Multi-modal Ensemble Models for Predicting Video Memorability](https://mlsanity.com/paper/2102.01173/) (2021)

## Built on by (most cited first, 3 of 3)

- [Experiences from the MediaEval Predicting Media Memorability Task](https://mlsanity.com/paper/2212.03955/) (2022)
- [Using Saliency and Cropping to Improve Video Memorability](https://mlsanity.com/paper/2309.11881/) (2023)
- [Generative Outpainting To Enhance the Memorability of Short-Form Videos](https://mlsanity.com/paper/2411.14213/) (2024)

## Abstract (arXiv)

This paper describes our approach to the Predicting Media Memorability task in MediaEval 2021, which aims to address the question of media memorability by setting the task of automatically predicting video memorability. This year we tackle the task from a comparative standpoint, looking to gain deeper insights into each of three explored modalities, and using our results from last year's submission (2020) as a point of reference. Our best performing short-term memorability model (0.132) tested on the TRECVid2019 dataset -- just like last year -- was a frame based CNN that was not trained on any TRECVid data, and our best short-term memorability model (0.524) tested on the Memento10k dataset, was a Bayesian Ride Regressor fit with DenseNet121 visual features.