# Generating Memorable Images Based on Human Visual Memory Schemas

arXiv: https://arxiv.org/abs/2005.02969 · HTML: https://mlsanity.com/paper/2005.02969/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 3 of 3)

- [Memorability: An image-computable measure of information utility](https://mlsanity.com/paper/2104.00805/) (2021)
- [Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification](https://mlsanity.com/paper/2301.06080/) (2022)
- [Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability](https://mlsanity.com/paper/2311.16484/) (2023)

## Abstract (arXiv)

This research study proposes using Generative Adversarial Networks (GAN) that incorporate a two-dimensional measure of human memorability to generate memorable or non-memorable images of scenes. The memorability of the generated images is evaluated by modelling Visual Memory Schemas (VMS), which correspond to mental representations that human observers use to encode an image into memory. The VMS model is based upon the results of memory experiments conducted on human observers, and provides a 2D map of memorability. We impose a memorability constraint upon the latent space of a GAN by employing a VMS map prediction model as an auxiliary loss. We assess the difference in memorability between images generated to be memorable or non-memorable through an independent computational measure of memorability, and additionally assess the effect of memorability on the realness of the generated images.