CHI-ANNOUNCEMENTS Archives

ACM SIGCHI General Interest Announcements (Mailing List)

CHI-ANNOUNCEMENTS@LISTSERV.ACM.ORG

Options: Use Forum View

Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Subject:
From:
Bogdan Ionescu <[log in to unmask]>
Reply To:
Bogdan Ionescu <[log in to unmask]>
Date:
Tue, 8 May 2018 00:55:09 +0300
Content-Type:
text/plain
Parts/Attachments:
text/plain (110 lines)
[Apologies for cross-postings]


*******************************************************
1st CALL FOR PARTICIPATION
Predicting Media Memorability Task
2018 MediaEval Benchmarking Initiative for Multimedia Evaluation
Website: http://www.multimediaeval.org/mediaeval2018/memorability/
*******************************************************
Register here: https://docs.google.com/forms/d/e/1FAIpQLSfw11pDSAJb92K6lLH0DU3r85NMOj1Ww2A5R01iqQE985fqdg/viewform
*******************************************************

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 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 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
***********************
Data is composed of 10,000 short (soundless) videos extracted from raw
footage used by professionals when creating content. 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 29-31 October 2018
at EURECOM, Sophia Antipolis, France. Working notes proceedings are to
appear with CEUR Workshop Proceedings (ceur-ws.org).


******************************
Important dates (tentative)
******************************
Development data release: 24 May
Test data release: 25 June
Runs due: 1 October
Working notes papers due: 17 October
MediaEval Workshop, Sophia Antipolis, France: 29-31 October


***********************
Task coordination
***********************
Romain Cohendet, Technicolor, France (romain.cohendet at technicolor.com)
Claire-Hélène Demarty, Technicolor, France (claire-helene.demarty at
technicolor.com)
Quang-Khanh-Ngoc Duong, Technicolor, France
Bogdan Ionescu, University Politehnica of Bucharest, Romania
Mats Sjöberg, Aalto University, Finland
Thanh-Toan Do, ARC Center of Excellence for Robotic Vision (ACRV), The
University of Adelaide, Australia


On behalf of the organizers,

Prof. Bogdan IONESCU
ETTI - University Politehnica of Bucharest
http://campus.pub.ro/lab7/bionescu/

Research Center CAMPUS
http://www.campus.pub.ro
https://facebook.com/upbcampus
https://twitter.com/upbcampus

    ---------------------------------------------------------------
    For news of CHI books, courses & software, join CHI-RESOURCES
     mailto: [log in to unmask]

    To unsubscribe from CHI-ANNOUNCEMENTS send an email to
     mailto:[log in to unmask]

    For further details of CHI lists see http://listserv.acm.org
    ---------------------------------------------------------------

ATOM RSS1 RSS2