# An Annotated Video Dataset for Computing Video Memorability

arXiv: https://arxiv.org/abs/2112.02303 · HTML: https://mlsanity.com/paper/2112.02303/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability](https://mlsanity.com/paper/1812.01973/) (2018)
- [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)

## Built on by (most cited first, 9 of 9)

- [Long-Term Ad Memorability: Understanding & Generating Memorable Ads](https://mlsanity.com/paper/2309.00378/) (2023)
- [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)
- [Experiences from the MediaEval Predicting Media Memorability Task](https://mlsanity.com/paper/2212.03955/) (2022)
- [Teaching Human Behavior Improves Content Understanding Abilities Of LLMs](https://mlsanity.com/paper/2405.00942/) (2024)
- [A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness](https://mlsanity.com/paper/2207.04762/) (2022)
- [Analysing the Memorability of a Procedural Crime-Drama TV Series, CSI](https://mlsanity.com/paper/2208.03479/) (2022)
- [Unsupervised Memorability Modeling from Tip-of-the-Tongue Retrieval Queries](https://mlsanity.com/paper/2511.20854/) (2025)
- [How to Take a Memorable Picture? Empowering Users with Actionable Feedback](https://mlsanity.com/paper/2602.21877/) (2026)

## Abstract (arXiv)

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.