[Apologies for cross-postings]
******************************************************* TASK PRE-RELEASE Predicting Video Memorability Task 2019 MediaEval Benchmarking Initiative for Multimedia Evaluation http://www.multimediaeval.org/mediaeval2019/ ******************************************************* The Predicting Video 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 dataset 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, reflects objective measures of memory performance. In contrast to previous work on image memorability prediction, where memorability was measured a few minutes after memorization, the dataset 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 memorization, 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. Optionally, descriptive titles attached to the videos may be used. 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 the impact of multimedia on perception 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 *********************** Data is composed of 10,000 short (soundless) videos extracted from raw footage used by professionals when creating content. 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. Memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorization of the videos, and then 24 to 72 hours later. The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Dense SIFT, HoG descriptors, LBP, GIST, Color Histogram, MFCC, Fc7 layer from AlexNet, C3D features, etc. ****************************** Workshop ****************************** Participants to the task are invited to present their results during the annual MediaEval Workshop, which will be held by the end of October 2019, in Nice, France, co-located with ACM Multimedia 2019. Working notes proceedings are to appear with CEUR Workshop Proceedings (ceur-ws.org). ****************************** Important dates (tentative) ****************************** Participant registration: March-May Development data release: 1 May Test data release: 3 June Runs due: 20 September Working notes papers due: 11 October MediaEval Workshop, Nice, France: End of October (co-located with ACM Multimedia 2019) *********************** Task coordination *********************** Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania Bogdan Ionescu, University Politehnica of Bucharest, Romania Claire-Hélène Demarty, Technicolor, France Quang-Khanh-Ngoc Duong, Technicolor, France Xavier Alameda-Pineda, INRIA, France Mats Sjöberg, CSC, Finland On behalf of the Organizers, Prof. Bogdan IONESCU ETTI - University Politehnica of Bucharest http://campus.pub.ro/lab7/bionescu/ _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
