Dear all, we are happy to announce MultiMediate’23! This year the challenge focuses on bodily behaviour recognition and engagement estimation, while continuing to invite submissions to previous years’ tasks. All datasets are available for download and you can start working on the challenge tasks. I am pasting the call for participation below. Feel free to forward it to interested colleagues. If you have any questions, please do not hesitate to contact myself or the other organisers. Best regards, Philipp Mueller Call for Participation - ACM Multimedia 2023 Grand Challenge: == MultiMediate: Multi-modal Behaviour Analysis for Artificial Mediation == https://multimediate-challenge.org/ Artificial mediators are a promising approach to support conversations, but at present their abilities are limited by insufficient progress in behaviour sensing and analysis. The MultiMediate challenge is designed to work towards the vision of effective artificial mediators by facilitating and measuring progress on key social behaviour sensing and analysis tasks. This year, the challenge focuses on the recognition of bodily behaviours as well as on engagement estimation. In addition, we continue to accept submissions to previous years’ tasks, including backchannel detection, agreement estimation from backchannels, eye contact detection, and next speaker prediction. == Bodily Behaviour Recognition Task == Bodily behaviours like fumbling, gesturing or crossed arms are key signals in social interactions and are related to many higher-level attributes including liking, attractiveness, social verticality, stress, and anxiety. While impressive progress was made on human body- and hand pose estimation, the recognition of such more complex bodily behaviours is still underexplored. We formulate bodily behaviour recognition as a 14-class multi-label classification. This task is based on the recently released BBSI annotations collected on the MPIIGroupInteraction dataset. This dataset consists of video- and audio recordings of participants engaged in a group discussion. Challenge participants will receive 64-frame video snippets as input and need to classify which of 14 behaviour classes are present. To counter class imbalances, performance will be evaluated using macro averaged average precision. == Engagement Estimation Task == Knowing how engaged participants are is important for a mediator whose goal it is to keep engagement at a high level. For the purpose of this challenge, we collected novel annotations of engagement on the Novice-Expert Interaction (NoXi) database. This database consists of dyadic, screen-mediated interactions focussed on information exchange. Interactions took place in several languages, and participants were recorded with video cameras and microphones. The task includes the continuous, frame-wise prediction of the level of conversational engagement of each participant on a continuous scale from 0 (lowest) to 1 (highest). We will use the Concordance Correlation Coefficient (CCC) to evaluate predictions. == Continuing Tasks == We continue to invite submission to the tasks introduced in MultiMedaite’21 and MultiMediate’22: Eye contact detection, next speaker prediction, backchannel detection, and agreement estimation from backchannels. All of these tasks make use of the MPIIGroupInteraction dataset. == Dataset & Evaluation Protocol == Training datasets for all tasks are available from our website. We will additionally provide baseline implementations along with pre-computed features to minimise the overhead for participants. The test sets for bodily behaviour recognition and engagement estimation will be released two weeks before the challenge deadline. Participants will in turn submit their predictions for evaluation against ground truth on our servers. For previous years’ tasks, the test sets are already published and three evaluations on the test set can be performed per month. == How to Participate == Instructions are available at https://multimediate-challenge.org/ Paper submission deadline: 14 July 2023 AOE == Organisers == Philipp Müller (German Research Center for Artificial Intelligence) Tobias Baur (Augsburg University) Dominik Schiller (Augsburg University) Michael Dietz (Augsburg University) Alexander Heimerl (Augsburg University) Elisabeth André (Augsburg University) Dominike Thomas (University of Stuttgart) Andreas Bulling (University of Stuttgart) Michal Balazia (INRIA Sophia Antipolis) François Brémond (INRIA Sophia Antipolis) -- Dr. Philipp Müller Senior Researcher, DFKI GmbH, Campus D3 2 Stuhlsatzenhausweg 3 D-66123 Saarbrücken, Germany Email: philipp.mueller@dfki.de -------------------------------------------------------------- Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Trippstadter Strasse 122, D-67663 Kaiserslautern, Germany Geschäftsführung: Prof. Dr. Antonio Krüger (Vorsitzende) Helmut Ditzer Vorsitzender des Aufsichtsrats: Dr. Ferri Abolhassan Amtsgericht Kaiserslautern, HRB 2313 --------------------------------------------------------------