# Memorability: An image-computable measure of information utility

arXiv: https://arxiv.org/abs/2104.00805 · HTML: https://mlsanity.com/paper/2104.00805/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 120 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [In-Domain GAN Inversion for Real Image Editing](https://mlsanity.com/paper/2004.00049/) (2020)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [On the "steerability" of generative adversarial networks](https://mlsanity.com/paper/1907.07171/) (2019)
- [Moments in Time Dataset: one million videos for event understanding](https://mlsanity.com/paper/1801.03150/) (2018)
- [The Secrets of Salient Object Segmentation](https://mlsanity.com/paper/1406.2807/) (2014)
- [GANalyze: Toward Visual Definitions of Cognitive Image Properties](https://mlsanity.com/paper/1906.10112/) (2019)
- [Semantic Photo Manipulation with a Generative Image Prior](https://mlsanity.com/paper/2005.07727/) (2020)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [Inverting The Generator Of A Generative Adversarial Network](https://mlsanity.com/paper/1611.05644/) (2016)
- [Towards End-to-end Video-based Eye-Tracking](https://mlsanity.com/paper/2007.13120/) (2020)
- [SoDeep: a Sorting Deep net to learn ranking loss surrogates](https://mlsanity.com/paper/1904.04272/) (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)
- [AMNet: Memorability Estimation with Attention](https://mlsanity.com/paper/1804.03115/) (2018)
- [MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking](https://mlsanity.com/paper/1912.07748/) (2019)
- [Defining Image Memorability using the Visual Memory Schema](https://mlsanity.com/paper/1903.02056/) (2019)
- [Is Image Memorability Prediction Solved?](https://mlsanity.com/paper/1901.11420/) (2019)
- [Show and Recall: Learning What Makes Videos Memorable](https://mlsanity.com/paper/1707.05357/) (2017)
- [Changing the Image Memorability: From Basic Photo Editing to GANs](https://mlsanity.com/paper/1811.03825/) (2018)
- [How to Make an Image More Memorable? A Deep Style Transfer Approach](https://mlsanity.com/paper/1704.01745/) (2017)
- [Understanding and Predicting the Memorability of Outdoor Natural Scenes](https://mlsanity.com/paper/1810.06679/) (2018)

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

- [Image Memorability Prediction with Vision Transformers](https://mlsanity.com/paper/2301.08647/) (2023)
- [What Images are More Memorable to Machines?](https://mlsanity.com/paper/2211.07625/) (2022)
- [Modeling Visual Memorability Assessment with Autoencoders Reveals Characteristics of Memorable Images](https://mlsanity.com/paper/2410.15235/) (2024)
- [Unforgettable Lessons from Forgettable Images: Intra-Class Memorability Matters in Computer Vision](https://mlsanity.com/paper/2412.20761/) (2024)
- [CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation](https://mlsanity.com/paper/2603.09286/) (2026)
- [Correlates of Image Memorability in Vision Encoders: Activations, Attention Entropy, Patch Uniformity and Autoencoder Losses](https://mlsanity.com/paper/2509.01453/) (2025)
- [How to Take a Memorable Picture? Empowering Users with Actionable Feedback](https://mlsanity.com/paper/2602.21877/) (2026)
- [Human-Inspired Context-Selective Multimodal Memory for Social Robots](https://mlsanity.com/paper/2604.12081/) (2026)

## Abstract (arXiv)

The pixels in an image, and the objects, scenes, and actions that they compose, determine whether an image will be memorable or forgettable. While memorability varies by image, it is largely independent of an individual observer. Observer independence is what makes memorability an image-computable measure of information, and eligible for automatic prediction. In this chapter, we zoom into memorability with a computational lens, detailing the state-of-the-art algorithms that accurately predict image memorability relative to human behavioral data, using image features at different scales from raw pixels to semantic labels. We discuss the design of algorithms and visualizations for face, object, and scene memorability, as well as algorithms that generalize beyond static scenes to actions and videos. We cover the state-of-the-art deep learning approaches that are the current front runners in the memorability prediction space. Beyond prediction, we show how recent A.I. approaches can be used to create and modify visual memorability. Finally, we preview the computational applications that memorability can power, from filtering visual streams to enhancing augmented reality interfaces.