Philipp Müller, Michal Balazia, Tobias Baur, Michael Dietz, Alexander Heimerl, Anna Penzkofer, Dominik Schiller, François Brémond, Jan Alexandersson, Elisabeth André, Andreas Bulling
{"title":"MultiMediate'24: Multi-Domain Engagement Estimation","authors":"Philipp Müller, Michal Balazia, Tobias Baur, Michael Dietz, Alexander Heimerl, Anna Penzkofer, Dominik Schiller, François Brémond, Jan Alexandersson, Elisabeth André, Andreas Bulling","doi":"arxiv-2408.16625","DOIUrl":null,"url":null,"abstract":"Estimating the momentary level of participant's engagement is an important\nprerequisite for assistive systems that support human interactions. Previous\nwork has addressed this task in within-domain evaluation scenarios, i.e.\ntraining and testing on the same dataset. This is in contrast to real-life\nscenarios where domain shifts between training and testing data frequently\noccur. With MultiMediate'24, we present the first challenge addressing\nmulti-domain engagement estimation. As training data, we utilise the NOXI\ndatabase of dyadic novice-expert interactions. In addition to within-domain\ntest data, we add two new test domains. First, we introduce recordings\nfollowing the NOXI protocol but covering languages that are not present in the\nNOXI training data. Second, we collected novel engagement annotations on the\nMPIIGroupInteraction dataset which consists of group discussions between three\nto four people. In this way, MultiMediate'24 evaluates the ability of\napproaches to generalise across factors such as language and cultural\nbackground, group size, task, and screen-mediated vs. face-to-face interaction.\nThis paper describes the MultiMediate'24 challenge and presents baseline\nresults. In addition, we discuss selected challenge solutions.","PeriodicalId":501480,"journal":{"name":"arXiv - CS - Multimedia","volume":"9 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.16625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Estimating the momentary level of participant's engagement is an important
prerequisite for assistive systems that support human interactions. Previous
work has addressed this task in within-domain evaluation scenarios, i.e.
training and testing on the same dataset. This is in contrast to real-life
scenarios where domain shifts between training and testing data frequently
occur. With MultiMediate'24, we present the first challenge addressing
multi-domain engagement estimation. As training data, we utilise the NOXI
database of dyadic novice-expert interactions. In addition to within-domain
test data, we add two new test domains. First, we introduce recordings
following the NOXI protocol but covering languages that are not present in the
NOXI training data. Second, we collected novel engagement annotations on the
MPIIGroupInteraction dataset which consists of group discussions between three
to four people. In this way, MultiMediate'24 evaluates the ability of
approaches to generalise across factors such as language and cultural
background, group size, task, and screen-mediated vs. face-to-face interaction.
This paper describes the MultiMediate'24 challenge and presents baseline
results. In addition, we discuss selected challenge solutions.