An Annotated Video Dataset for Computing Video Memorability

• Published in: arXiv.org
• Main Authors: Rukiye Savran Kiziltepe, Sweeney, Lorin, Constantin, Mihai Gabriel, Doctor, Faiyaz, Alba Garcia Seco de Herrera, Claire-Helene Demarty, Healy, Graham, Ionescu, Bogdan, Smeaton, Alan F
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 04.12.2021
• Subjects: Annotations; Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition; Datasets; Feature extraction; Recognition; Video
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2112.02303

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.