Connectionists: 1st Call for Participation & registration: MediaEval 2021 Predicting Video Memorability Task

Thu Sep 16 10:52:15 EDT 2021

[Apologies for cross-postings]

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1st CALL FOR PARTICIPATION & DEVELOPMENT DATA RELEASE

Predicting Video Memorability Task

2021 MediaEval Benchmarking Initiative for Multimedia Evaluation

https://multimediaeval.github.io/editions/2021/tasks/memorability/

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Register to participate by filling in the MediaEval 2021 Registration form:
https://docs.google.com/forms/d/e/1FAIpQLSchIcIaSlM1fNeWGCSoSBMR6HS48HKMhWEY151vvCmb5KhO-w/viewform

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Annotations: https://annotator.uk/mediaeval/index.php

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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 memorisation, 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 memorisation, we expect long-term memorability
annotations to be more representative of long-term memory performance,
which is used preferably in numerous applications.

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Video-based prediction task

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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.

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Generalization task (optional)

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The aim of the Generalization subtask is to check system performance on
other types of video data. Participants will use their systems, trained on
one of the two sources of data we propose, to predict the memorability of
videos from the testing set of the other source of data. We believe this
would provide interesting insights into the performance of the developed
systems, given that, while the two sources of data measure memorability in
a similar way, the videos may be somewhat different with regards to their
content, general subjects or length. As this will be an optional task,
participants are not required to participate in it.

Pilot demonstration task (pilot)

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The aim of the Memorability-EEG pilot task is to promote interest in the
use of neural signals—either alone, or in combination with other data
sources—in the context of predicting video memorability by demonstrating
what EEG data can provide. The dataset will be a set of features
pre-extracted from the EEG for a subset of videos from task 1. This
demonstration pilot will enable interested researchers to see how they
could use neural signals without any of the requisite domain knowledge in a
future Memorability task, potentially increasing interdisciplinary interest
in the subject of memorability, and opening the door to novel EEG-computer
vision combined approaches to predicting video memorability.

Pre-selected participants in this pilot demonstration will use the dataset
to explore all manners of machine learning and processing strategies to
predict video memorability. This will lead to a presentation on their
findings, which will ultimately contribute towards the collaborative
definition of a fully-fledged task at MediaEval 2022, where participating
teams will submit runs and be benchmarked.

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Target communities

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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).

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Data

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The first dataset is composed of a subset of 6,000 short videos retrieved
from TRECVid 2019 Video to Text dataset [1]. 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 [2], 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: Histograms in the
HSV and RGB spaces, HOG, LBP, and deep features extracted from AlexNet, VGG
and C3D. In comparison to the videos used for 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.

Additionally, we will open the Memento10k dataset to participants. This
dataset contains 10.000 three-second videos depicting in-the-wild scenes,
with their associated short term memorability scores, memorability decay
values, action labels, and 5 accompanying captions. 7000 videos will be
released as a training set, and 1500 will be given for validation. The last
1500 videos will be used as the test set for scoring submissions. The
scores are computed with 90 annotations per video on average, and the
videos were deafened before being shown to participants. We will also
distribute a set of features for each video analogous to the Trecvid set.

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Annotations

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We need more annotations for the dataset. We kindly ask for your help to
get more annotations. Please visit the link (
https://annotator.uk/mediaeval/index.php) and participate in the funny game
to contribute to the dataset and get familiar with the data. Thanks in
advance for your contribution

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Workshop

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Participants to the task are invited to present their results during the
annual MediaEval Workshop, which will be held in Bergen, Norway with
opportunity for online, on 6-8 December 2021. Working notes proceedings are
to appear with CEUR Workshop Proceedings (ceur-ws.org).

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Important dates (tentative)

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(open) Participant registration: July

Data release: 15 September

Runs due: 11 November

Working notes papers due: 22 November

MediaEval Workshop: 6-8 December, in Bergen, Norway with opportunity for
online participation

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Task coordination

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Alba García Seco de Herrera, <alba.garcia(at)essex.ac.uk>, University of
Essex, UK

Rukiye Savran Kiziltepe, <rs16419(at)essex.ac.uk>, University of Essex, UK

Mihai Gabriel Constantin, <cmihaigabriel(at)gmail.com>, University
Politehnica of Bucharest, Romania

Bogdan Ionescu, University Politehnica of Bucharest, Romania

Alan Smeaton, Graham Healy, Dublin City University, Ireland

Claire-Hélène Demarty, InterDigital, R&I, France

Sebastian Halder, University of Essex, UK

Ana Matrán-Fernández, University of Essex, UK

Camilo Fosco, Massachusetts Institute of Technology Cambridge,
Massachusetts, USA

Lorin Sweeney, Dublin City University, Ireland

Graham Healy, Dublin City University, Ireland

On behalf of the Organizers,

Alba García Seco de Herrera

Dr Alba García Seco de Herrera

Department of Computer Science and Electronic Engineering (CSEE)

University of Essex

https://www.essex.ac.uk/people/garci58409/alba-garcia-seco-de-herrera
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