# Overview of The MediaEval 2021 Predicting Media Memorability Task

arXiv: https://arxiv.org/abs/2112.05982 · HTML: https://mlsanity.com/paper/2112.05982/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability](https://mlsanity.com/paper/1812.01973/) (2018)
- [Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability](https://mlsanity.com/paper/2009.02568/) (2020)
- [TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval](https://mlsanity.com/paper/2009.09984/) (2020)
- [Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?](https://mlsanity.com/paper/2012.15650/) (2020)
- [An Annotated Video Dataset for Computing Video Memorability](https://mlsanity.com/paper/2112.02303/) (2021)
- [Show and Recall: Learning What Makes Videos Memorable](https://mlsanity.com/paper/1707.05357/) (2017)
- [MediaEval 2018: Predicting Media Memorability Task](https://mlsanity.com/paper/1807.01052/) (2018)

## Built on by (most cited first, 5 of 5)

- [Overview of The MediaEval 2022 Predicting Video Memorability Task](https://mlsanity.com/paper/2212.06516/) (2022)
- [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)
- [Using Saliency and Cropping to Improve Video Memorability](https://mlsanity.com/paper/2309.11881/) (2023)

## Abstract (arXiv)

This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. In 2021, two datasets of videos are used: first, a subset of the TRECVid 2019 Video-to-Text dataset; second, the Memento10K dataset in order to provide opportunities to explore cross-dataset generalisation. In addition, an Electroencephalography (EEG)-based prediction pilot subtask is introduced. In this paper, we outline the main aspects of the task and describe the datasets, evaluation metrics, and requirements for participants' submissions.