# Is Image Memorability Prediction Solved?

## TL;DR

This paper presents an algorithm that predicts image memorability at human-level accuracy on a large dataset, discusses the limitations of current benchmarks, and suggests improvements for future evaluation methods.

## Contribution

The paper introduces a CNN-based algorithm achieving human-level memorability prediction and critically examines the adequacy of existing benchmarks.

## Key findings

- Achieved human-level performance on LaMem dataset
- Identified limitations in current memorability benchmarks
- Provided recommendations for future benchmark design

## Abstract

This paper deals with the prediction of the memorability of a given image. We start by proposing an algorithm that reaches human-level performance on the LaMem dataset - the only large scale benchmark for memorability prediction. The suggested algorithm is based on three observations we make regarding convolutional neural networks (CNNs) that affect memorability prediction. Having reached human-level performance we were humbled, and asked ourselves whether indeed we have resolved memorability prediction - and answered this question in the negative. We studied a few factors and made some recommendations that should be taken into account when designing the next benchmark.