# Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?

arXiv: https://arxiv.org/abs/2012.15650 · HTML: https://mlsanity.com/paper/2012.15650/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [An Annotated Video Dataset for Computing Video Memorability](https://mlsanity.com/paper/2112.02303/) (2021)
- [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)
- [Overview of The MediaEval 2022 Predicting Video Memorability Task](https://mlsanity.com/paper/2212.06516/) (2022)
- [Multi-modal Ensemble Models for Predicting Video Memorability](https://mlsanity.com/paper/2102.01173/) (2021)
- [Predicting Media Memorability: Comparing Visual, Textual and Auditory Features](https://mlsanity.com/paper/2112.07969/) (2021)
- [Experiences from the MediaEval Predicting Media Memorability Task](https://mlsanity.com/paper/2212.03955/) (2022)
- [Analysing the Memorability of a Procedural Crime-Drama TV Series, CSI](https://mlsanity.com/paper/2208.03479/) (2022)
- [Computer Vision for Supporting Image Search](https://mlsanity.com/paper/2111.08772/) (2021)
- [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 the MediaEval 2020 textitPredicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video-to-Text dataset, containing more action rich video content as compared with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for participants' run submissions.