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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">[Apologies for cross-postings]</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">1st CALL FOR PARTICIPATION & DEVELOPMENT DATA RELEASE </span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Predicting Video Memorability Task</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">2023 MediaEval Benchmarking Initiative for Multimedia Evaluation</span></p>
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<a href="https://multimediaeval.github.io/editions/2023/tasks/memorability/" style="text-decoration:none" id="OWA6c0aa4dc-0a88-4e9c-c552-2322b073497b" class="OWAAutoLink ContentPasted0"><span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: underline; text-decoration-skip-ink: none; color: rgb(17, 85, 204);">https://multimediaeval.github.io/editions/2023/tasks/memorability/</span></a><span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0"> </span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Register to participate by filling in the MediaEval 2023 Registration form: </span></p>
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<a href="https://docs.google.com/forms/d/e/1FAIpQLSfu1TfBZ5PKN1MdTrJFjEKt7r2_qTZw-_WBG8VTgSpT7JWi9A/viewform" style="text-decoration:none" id="OWAc271c3b7-bf25-783e-3805-5b340fa7a5eb" class="OWAAutoLink ContentPasted0"><span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: underline; text-decoration-skip-ink: none; color: rgb(17, 85, 204);">https://docs.google.com/forms/d/e/1FAIpQLSfu1TfBZ5PKN1MdTrJFjEKt7r2_qTZw-_WBG8VTgSpT7JWi9A/viewform</span></a><span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0"> </span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">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. </span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Participants will be provided with an extensive dataset of videos with memorability annotations, related information,
pre-extracted state-of-the-art visual features, and electroencephalography (EEG) recordings. 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. The outputs of the prediction models – i.e., the predicted memorability
scores for the videos – will be compared with ground truth memorability scores using classic evaluation metrics (e.g., Spearman’s rank correlation).</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Generalization subtask 1</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">The aim of the Generalization subtask is to check system performance on other types of video data.
</span><span style="font-size: 11.5pt; font-family: Roboto, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Participants will train their system on one of the two sources of data we provide and will test them
on the other source of data.</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">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 from subjects as they viewed the videos, for a subset of
videos from task 1. </span><span style="font-size: 11.5pt; font-family: Roboto, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">This task requires participants to automatically predict if a person will remember
a video. Participants are required to generate automatic systems that predict if a person will remember a new video based on the given video dataset and their EEG record.</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Target communities</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">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, and machine learning (although not limited to these areas).</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Workshop</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Participants to the task are invited to present their results during the annual MediaEval Workshop, which will
be held in</span><span style="font-size: 11.5pt; font-family: Roboto, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0"> Bergen, Norway with opportunity for online, on 12-13 January 2023</span><span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">.
Working notes proceedings are to appear with CEUR Workshop Proceedings (ceur-ws.org).</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Important dates (tentative)</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">25 August 2023: Data release</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">30 November 2023: Runs due and results returned. Exact dates to be announced.</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">8 December 2023: Results returned</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">15 December 2023: Working notes paper</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">1-2 February 2024: 14th Annual MediaEval Workshop, Collocated with MMM 2024 in Amsterdam, Netherlands and also
online.</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Alba García Seco de Herrera, Sebastian Halder, Ana Matran-Fernandez, University of Essex, UK;</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Mihai Gabriel Constantin, Bogdan Ionescu, University Politehnica of Bucharest, Romania;</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Lorin Sweeney, Graham Healy, Alan Smeaton, Dublin City University, Ireland;</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Claire-Hélène Demarty, InterDigital, R&I, France;</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Camilo Fosco, Massachusetts Institute of Technology Cambridge, Massachusetts, USA;</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Rukiye Savran Kiziltepe, Karadeniz Technical University, Turkey.</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">On behalf of the Organisers,</span></p>
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<span style="font-size: 11pt; font-family: Arial, sans-serif; font-weight: 400; text-decoration: none; color: rgb(0, 0, 0);" class="ContentPasted0">Mihai Gabriel Constantin</span></p>
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