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From:
Bogdan Ionescu <[log in to unmask]>
Reply To:
Bogdan Ionescu <[log in to unmask]>
Date:
Sat, 1 Aug 2020 00:15:23 +0300
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[Apologies for cross-postings]

*******************************************************
Predicting Media Memorability Task
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: 21 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/

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