[Apologies for cross-postings] ******************************************************* Predicting Media Memorability Task #Development Data Released# 2020 MediaEval Benchmarking Initiative for Multimedia Evaluation https://multimediaeval.github.io/editions/2020/tasks/memorability/ ******************************************************* Register here to participate: https://docs.google.com/forms/d/e/1FAIpQLSfxS4LPBhLQUTXSPT5vogtiSy7BuAKrPs6u6pZXcSV1Xs7XEQ/viewform ******************************************************* Help us with the annotations: https://annotator.uk/mediaeval/index.php *******************************************************
The Predicting Media Memorability Task focuses on the problem of predicting how memorable a video will be. It requires participants to automatically predict memorability scores for videos, which reflect the probability of a video being remembered. Participants will be provided with an extensive data set of videos with memorability annotations, and pre-extracted state-of-the-art visual features. The ground truth has been collected through recognition tests, and, for this reason, it reflects objective measures of memory performance. In contrast to previous work on image memorability prediction, where memorability was measured a few minutes after memorisation, the data set comes with short-term and long-term memorability annotations. Because memories continue to evolve in long-term memory, in particular during the first day following memorisation, we expect long-term memorability annotations to be more representative of long-term memory performance, which is used preferably in numerous applications. Participants will be required to train computational models capable of inferring video memorability from visual content. Models will be evaluated through standard evaluation metrics used in ranking tasks. *********************** Target communities *********************** Researchers will find this task interesting if they work in the areas of human perception and scene understanding, such as image and video interestingness, memorability, attractiveness, aesthetics prediction, event detection, multimedia affect and perceptual analysis, multimedia content analysis, machine learning (though not limited to). *********************** Data & ground truth *********************** Data is composed of 6,000 short videos retrieved from TRECVid 2019 Video to Text data set. Each video consists of a coherent unit in terms of meaning and is associated with two scores of memorability that refer to its probability to be remembered after two different durations of memory retention. Similar to previous editions of the task, memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorisation of the videos (short term), and then 24 to 72 hours later (long term). The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Aesthetic Features, C3D, Captions, Colour Histograms, HMP, HoG, Fc7 layer from InceptionV3, LBP, or ORP. In comparison to the videos used in this task in 2018 and 2019, the TRECVid videos have much more action happening in them and thus are more interesting for subjects to view. *********************** MediaEval Workshop *********************** Participants to the task are invited to present their results during the annual MediaEval Workshop, which will be held online in early December 2020. Working notes proceedings are to appear with CEUR Workshop Proceedings (ceur-ws.org). *********************** Important dates (tentative) *********************** (open) Participant registration: July Development data release: 31 August Test data release: 21 September Runs due: 15 October Working notes papers due: 30 November MediaEval Workshop, online: Early December *********************** Task coordination *********************** Alba Garcia Seco de Herrera, Rukiye Savran Kiziltepe, Faiyaz Doctor, University of Essex, UK Mihai Gabriel Constantin, Bogdan Ionescu, University Politehnica of Bucharest, Romania Alan Smeaton, Graham Healy, Dublin City University, Ireland Claire-Helene Demarty, InterDigital, France On behalf of the Organizers, Prof. Bogdan IONESCU http://campus.pub.ro/lab7/bionescu/ _______________________________________________ uai mailing list uai@engr.orst.edu https://it.engineering.oregonstate.edu/mailman/listinfo/uai
