mlsanityData to 7 Oct 2026

An Annotated Video Dataset for Computing Video Memorability

arXiv 2112.02303 ↗First posted 4 Dec 2021cs.CV cs.AIcited by 9

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Abstract (arXiv)

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 4 of its 9 references that are in the map. First posted on arXiv 4 Dec 2021.

Builds on

4 of 9 references.

References · 4

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2018VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorabilitycited by 24
  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

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 1; 2025: 1; 2026: 1.

9 papers.