Scholarlink

<p>One of the powers of visual media lies in its ability to create a lasting impression on the viewer’s memory. In this digital age, where media is abundant and attention spans are fleeting, the task of predicting which content will stick in the viewer’s mind has become a critical challenge in computer vision. Computational memorability seeks to address this by developing models that estimate how memorable a piece of media is likely to be. In this review we focus on the MediaEval Predicting Video Memorability benchmark, a recurring evaluation task that has run annually since 2018. This benchmark provides a unique and consistent framework for researchers to compare and refine their memorability prediction techniques using standardised datasets and metrics. Its reproducible framework has proven invaluable for tracking progress and fostering innovation in this rapidly evolving domain. We analyse the evolution of the benchmark across its 2018–2023 editions, discussing the challenges that still remain, such as the need for more interpretability in models and the difficulty of predicting subjective and context-dependent memorability. By analysing and synthesising the collective insights gained from this task, we endeavour to inspire new avenues of inquiry and drive progress towards a more comprehensive understanding of this topic.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Review of Computational Memorability: A Benchmark Framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

查看全文
  • Mihai Gabriel Constantin,
  • Claire-Hélène Demarty,
  • Camilo Fosco,
  • Sebastian Halder,
  • Graham Healy,
  • Bogdan Ionescu,
  • Stefan Valentin Luncanu,
  • Iván Martín-Fernández,
  • Ana Matran-Fernandez,
  • Rukiye Savran Kiziltepe,
  • Alan F. Smeaton,
  • Liviu-Daniel Stefan,
  • Lorin Sweeney,
  • Alba García Seco de Herrera

摘要

One of the powers of visual media lies in its ability to create a lasting impression on the viewer’s memory. In this digital age, where media is abundant and attention spans are fleeting, the task of predicting which content will stick in the viewer’s mind has become a critical challenge in computer vision. Computational memorability seeks to address this by developing models that estimate how memorable a piece of media is likely to be. In this review we focus on the MediaEval Predicting Video Memorability benchmark, a recurring evaluation task that has run annually since 2018. This benchmark provides a unique and consistent framework for researchers to compare and refine their memorability prediction techniques using standardised datasets and metrics. Its reproducible framework has proven invaluable for tracking progress and fostering innovation in this rapidly evolving domain. We analyse the evolution of the benchmark across its 2018–2023 editions, discussing the challenges that still remain, such as the need for more interpretability in models and the difficulty of predicting subjective and context-dependent memorability. By analysing and synthesising the collective insights gained from this task, we endeavour to inspire new avenues of inquiry and drive progress towards a more comprehensive understanding of this topic.