Is Image Memorability Prediction Solved?

Shay Perera, Ayellet Tal, Lihi Zelnik-Manor; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

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.

Related Material


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[bibtex]
@InProceedings{Perera_2019_CVPR_Workshops,
author = {Perera, Shay and Tal, Ayellet and Zelnik-Manor, Lihi},
title = {Is Image Memorability Prediction Solved?},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}