# VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability

arXiv: https://arxiv.org/abs/1812.01973 · HTML: https://mlsanity.com/paper/1812.01973/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 24 of 24)

- [Ambiguous Images With Human Judgments for Robust Visual Event Classification](https://mlsanity.com/paper/2210.03102/) (2022)
- [An Annotated Video Dataset for Computing Video Memorability](https://mlsanity.com/paper/2112.02303/) (2021)
- [Long-Term Ad Memorability: Understanding & Generating Memorable Ads](https://mlsanity.com/paper/2309.00378/) (2023)
- [Memorability: An image-computable measure of information utility](https://mlsanity.com/paper/2104.00805/) (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)
- [Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understanding](https://mlsanity.com/paper/2407.08150/) (2024)
- [Multi-modal Ensemble Models for Predicting Video Memorability](https://mlsanity.com/paper/2102.01173/) (2021)
- [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)
- [Analysing the Memorability of a Procedural Crime-Drama TV Series, CSI](https://mlsanity.com/paper/2208.03479/) (2022)
- [Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability](https://mlsanity.com/paper/2311.16484/) (2023)
- [Introducing MeMo: A Multimodal Dataset for Memory Modelling in Multiparty Conversations](https://mlsanity.com/paper/2409.13715/) (2024)
- [Unforgettable Lessons from Forgettable Images: Intra-Class Memorability Matters in Computer Vision](https://mlsanity.com/paper/2412.20761/) (2024)
- [Shared memories driven by the intrinsic memorability of items](https://mlsanity.com/paper/2104.06937/) (2021)
- [Using Saliency and Cropping to Improve Video Memorability](https://mlsanity.com/paper/2309.11881/) (2023)
- [MindMem: Multimodal for Predicting Advertisement Memorability Using LLMs and Deep Learning](https://mlsanity.com/paper/2502.18371/) (2025)
- [Enhancing Video Memorability Prediction with Text-Motion Cross-modal Contrastive Loss and Its Application in Video Summarization](https://mlsanity.com/paper/2506.08649/) (2025)
- [The Emotion-Memory Link: Do Memorability Annotations Matter for Intelligent Systems?](https://mlsanity.com/paper/2507.14084/) (2025)
- [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)
- [Show Me When and Where: Towards Referring Video Object Segmentation in the Wild](https://mlsanity.com/paper/2603.14300/) (2026)
- [Predicted Cortex Is Not a Domain-General Prior: A Matched-Control Audit of Brain-Encoding Features for Video Memorability](https://mlsanity.com/paper/2607.16292/) (2026)

## Abstract (arXiv)

Humans share a strong tendency to memorize/forget some of the visual information they encounter. This paper focuses on providing computational models for the prediction of the intrinsic memorability of visual content. To address this new challenge, we introduce a large scale dataset (VideoMem) composed of 10,000 videos annotated with memorability scores. In contrast to previous work on image memorability -- where memorability was measured a few minutes after memorization -- memory performance is measured twice: a few minutes after memorization and again 24-72 hours later. Hence, the dataset comes with short-term and long-term memorability annotations. After an in-depth analysis of the dataset, we investigate several deep neural network based models for the prediction of video memorability. Our best model using a ranking loss achieves a Spearman's rank correlation of 0.494 for short-term memorability prediction, while our proposed model with attention mechanism provides insights of what makes a content memorable. The VideoMem dataset with pre-extracted features is publicly available.