mlsanityData to 14 Oct 2026

Experiences from the MediaEval Predicting Media Memorability Task

arXiv 2212.03955 ↗First posted 7 Dec 2022cs.CV cs.AIcited by 2

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Abstract (arXiv)

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time. This has allowed us to compare the performance of many memorability prediction techniques on the same data and in a reproducible way and to refine and improve on those techniques. The resources created to compute media memorability are now being used by researchers well beyond the actual evaluation campaign. In this paper we present a summary of the task, including the collective lessons we have learned for the research community.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 7 Dec 2022.

Builds on

8 of 16 references.

References · 8

  1. 2018VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorabilitycited by 24
  2. 2020Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorabilitycited by 23
  3. 2020TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrievalcited by 16
  4. 2020Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?cited by 11
  5. 2021An Annotated Video Dataset for Computing Video Memorabilitycited by 9
  6. 2021Overview of The MediaEval 2021 Predicting Media Memorability Taskcited by 5
  7. 2021Predicting Media Memorability: Comparing Visual, Textual and Auditory Featurescited by 3
  8. 2021Overview of the EEG Pilot Subtask at MediaEval 2021: Predicting Media Memorabilitycited by 2

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.