# Show and Recall: Learning What Makes Videos Memorable

arXiv: https://arxiv.org/abs/1707.05357 · HTML: https://mlsanity.com/paper/1707.05357/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 8 of 8)

- [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)
- [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)
- [Shared memories driven by the intrinsic memorability of items](https://mlsanity.com/paper/2104.06937/) (2021)
- [A Dataset and Baselines for Measuring and Predicting the Music Piece Memorability](https://mlsanity.com/paper/2405.12847/) (2024)
- [LLM-based Fusion of Multi-modal Features for Commercial Memorability Prediction](https://mlsanity.com/paper/2510.22829/) (2025)

## Abstract (arXiv)

With the explosion of video content on the Internet, there is a need for research on methods for video analysis which take human cognition into account. One such cognitive measure is memorability, or the ability to recall visual content after watching it. Prior research has looked into image memorability and shown that it is intrinsic to visual content, but the problem of modeling video memorability has not been addressed sufficiently. In this work, we develop a prediction model for video memorability, including complexities of video content in it. Detailed feature analysis reveals that the proposed method correlates well with existing findings on memorability. We also describe a novel experiment of predicting video sub-shot memorability and show that our approach improves over current memorability methods in this task. Experiments on standard datasets demonstrate that the proposed metric can achieve results on par or better than the state-of-the art methods for video summarization.