# Overview of The MediaEval 2022 Predicting Video Memorability Task

arXiv: https://arxiv.org/abs/2212.06516 · HTML: https://mlsanity.com/paper/2212.06516/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## 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)
- [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)
- [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)
- [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)
- [Overview of the EEG Pilot Subtask at MediaEval 2021: Predicting Media Memorability](https://mlsanity.com/paper/2201.00620/) (2021)

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

- [Diffusing Surrogate Dreams of Video Scenes to Predict Video Memorability](https://mlsanity.com/paper/2212.09308/) (2022)
- [Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability](https://mlsanity.com/paper/2311.16484/) (2023)
- [Using Saliency and Cropping to Improve Video Memorability](https://mlsanity.com/paper/2309.11881/) (2023)
- [Memories in the Making: Predicting Video Memorability with Encoding Phase EEG](https://mlsanity.com/paper/2309.16704/) (2023)
- [Generative Outpainting To Enhance the Memorability of Short-Form Videos](https://mlsanity.com/paper/2411.14213/) (2024)

## Abstract (arXiv)

This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022. This year we have reorganised and simplified the task in order to lubricate a greater depth of inquiry. Similar to last year, two datasets are provided in order to facilitate generalisation, however, this year we have replaced the TRECVid2019 Video-to-Text dataset with the VideoMem dataset in order to remedy underlying data quality issues, and to prioritise short-term memorability prediction by elevating the Memento10k dataset as the primary dataset. Additionally, a fully fledged electroencephalography (EEG)-based prediction sub-task is introduced. In this paper, we outline the core facets of the task and its constituent sub-tasks; describing the datasets, evaluation metrics, and requirements for participant submissions.