Is Image Memorability Prediction Solved?

Shay Perera, Ayellet Tal, Lihi Zelnik-Manor

CVPRW 2019 pp. 800-808

doi:10.1109/CVPRW.2019.00108 /cvprw/2019/perera2019cvprw-image/

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.

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Cite

Text

Perera et al. "Is Image Memorability Prediction Solved?." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00108

Markdown

[Perera et al. "Is Image Memorability Prediction Solved?." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/perera2019cvprw-image/) doi:10.1109/CVPRW.2019.00108

BibTeX

@inproceedings{perera2019cvprw-image,
  title     = {{Is Image Memorability Prediction Solved?}},
  author    = {Perera, Shay and Tal, Ayellet and Zelnik-Manor, Lihi},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {800-808},
  doi       = {10.1109/CVPRW.2019.00108},
  url       = {https://mlanthology.org/cvprw/2019/perera2019cvprw-image/}
}