research-article

Increasing Image Memorability with Neural Style Transfer

Authors:

Aliaksandr Siarohin,

Cveta Majtanovic,

Xavier Alameda-Pineda,

Nicu SebeAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 15, Issue 2

Article No.: 42, Pages 1 - 22

https://doi.org/10.1145/3311781

Published: 05 June 2019 Publication History

Abstract

Recent works in computer vision and multimedia have shown that image memorability can be automatically inferred exploiting powerful deep-learning models. This article advances the state of the art in this area by addressing a novel and more challenging issue: “Given an arbitrary input image, can we make it more memorable?” To tackle this problem, we introduce an approach based on an editing-by-applying-filters paradigm: given an input image, we propose to automatically retrieve a set of “style seeds,” i.e., a set of style images that, applied to the input image through a neural style transfer algorithm, provide the highest increase in memorability. We show the effectiveness of the proposed approach with experiments on the publicly available LaMem dataset, performing both a quantitative evaluation and a user study. To demonstrate the flexibility of the proposed framework, we also analyze the impact of different implementation choices, such as using different state-of-the-art neural style transfer methods. Finally, we show several qualitative results to provide additional insights on the link between image style and memorability.

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Index Terms

Increasing Image Memorability with Neural Style Transfer
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 15, Issue 2

May 2019

375 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3339884

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2019 ACM.

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Publication History

Published: 05 June 2019

Accepted: 01 February 2019

Revised: 01 February 2019

Received: 01 May 2018

Published in TOMM Volume 15, Issue 2

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Jiao JCao X(2024)Research on designers’ behavioral intention toward Artificial Intelligence-Aided Design: integrating the Theory of Planned Behavior and the Technology Acceptance ModelFrontiers in Psychology10.3389/fpsyg.2024.145071715Online publication date: 16-Sep-2024
https://doi.org/10.3389/fpsyg.2024.1450717
Niu YWu LZhang YZhu YZhu GWang J(2024)Multi-Model Style-Aware Diffusion Learning for Semantic Image SynthesisACM Transactions on Multimedia Computing, Communications, and Applications10.1145/368615520:11(1-21)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3686155
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