MultiMediate 2025

Here, we introduce the different challenge tasks, evaluation methodology and rules for participation.

Baseline approaches are available at https://git.opendfki.de/philipp.mueller/multimediate23

Cross-cultural Multi-domain Engagement Estimation

Knowing how engaged participants are is important for a mediator whose goal it is to keep engagement at a high level. The task of engagement estimation involves 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 provide training- and test datasets from different domains, including variations in language and cultural background as well as different social situations. Participants are encouraged to investigate multimodal as well as reciprocal behaviour of both interlocutors. We will use the Concordance Correlation Coefficient (CCC) to evaluate predictions. The overall performance of a team will be evaluated by taking the average CCC across the four different test datasets. NOXI (MultiMediate’23 version): A dataset of novice-expert interactions recorded with microphones and video cameras, and with frame-wise engagement annotations. This dataset is identical to the engagement estimation challenge in MultiMediate'23 and consists of recordings in English, French and German. Training, validation and test sets are available. NOXI (additional languages): This data is used as a test set only and includes four languages that are not part of the original NOXI dataset: two sessions in Arabic, two in Italian, four in Indonesian, and four in Spanish. As a result, this evaluation set tests the ability of participants' approaches to transfer to new languages and cultural backgrounds not seen at training time. NOXI+J: A novel dataset collected according to the NOXI protocol, but in Japan with Japanese and Chinese speakers. Training, validation and test sets are available. MPIIGroupInteraction: We collected engagement annotations on the MPIIGroupInteraction test and validation sets. The validation set with ground truth annotations will be provided to participants to monitor their performance on the out-of-domain task. In addition it may be used as a limited set of training data to develop supervised domain adaptation approaches. The following Table gives an overview over the datasets used in the cross-cultural multi-domain engagement estimation challenge. Languages covered by each dataset are given in italics, with the respective number of interactions in parentheses.

In the following, we will present the different datasets that are included in the challenge. All of them will be used as test data, some of them are available for training. To take part in the challenge, participants need to submit predictions on all four test datasets.

• NOXI (MultiMediate’23 version): : A dataset of novice-expert interactions recorded with microphones and video cameras, and with frame-wise engagement annotations. This dataset is identical to the engagement estimation challenge in MultiMediate'23 and consists of recordings in English, French and German. Training, validation and test sets are available.
• NOXI (additional languages): This data is used as a test set only and includes four languages that are not part of the original NOXI dataset: two sessions in Arabic, two in Italian, four in Indonesian, and four in Spanish. As a result, this evaluation set tests the ability of participants' approaches to transfer to new languages and cultural backgrounds not seen at training time.
• NOXI+J (additional languages and culture): A novel dataset collected according to the NOXI protocol, but in Japan with Japanese and Chinese speakers. Training, validation and test sets are available.
• MPIIGroupInteraction: We collected engagement annotations on the MPIIGroupInteraction test and validation sets. The validation set with ground truth annotations will be provided to participants to monitor their performance on the out-of-domain task. In addition it may be used as a limited set of training data to develop supervised domain adaptation approaches.

The following Table gives an overview over the datasets used in the cross-cultural multi-domain engagement estimation challenge. Languages covered by each dataset are given in italics, with the respective number of interactions in parentheses.

Continuing MultiMediate Tasks

In addition to the two tasks described above we also invite submission to the three most popular tasks included in MultiMediate’21-’23.

Bodily Behaviour Recognition

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. With the bodily behaviour recognition task, we present the first challenge addressing this problem. We formulate bodily behaviour recognition as a 14-class multi-label classification. This task is based on the recently released BBSI dataset (Balazia et al., 2022). Challenge participants will receive 64-frame video snippets as input and need output a score indicating the likelihood of each behaviour class being present. To counter class imbalances, performance will be evaluated using macro averaged average precision. We encourage all participants in the bodily behaviour recognition task to also evaluate their approaches on the related Micro-Action Analysis Challenge (MAC 2025) (https://sites.google.com/view/micro-action). Strong results in both challenges will improve the chances of your papers being accepted. However, please be aware that we do not accept double submissions, i.e. you should submit your paper to only one of the two challenges (either MultiMediate’25 or MAC’25).

Backchannel Detection (Multimediate'22 task)

Backchannels serve important meta-conversational purposes like signifying attention or indicating agreement. They can be expressed in a variety of ways - ranging from vocal behaviour (“yes”, “ah-ha”) to subtle nonverbal cues like head nods or hand movements. The backchannel detection sub-challenge focuses on classifying whether a participant of a group interaction expresses a backchannel at a given point in time. Challenge participants will be required to perform this classification based on a 10-second context window of audiovisual recordings of the whole group. Approaches will be evaluated using classification accuracy.

Eye Contact Detection (MultiMediate’21 task)

We define eye contact as a discrete indication of whether a participant is looking at another participant’s face, and if so, who this other participant is. Video and audio recordings over a 10 second context window will be provided as input to provide temporal context for the classification decision. Eye contact has to be detected for the last frame of the 10-second context window. In the next speaker prediction sub-challenge, participants need to predict the speaking status of each participant at one second after the end of the context window. Approaches will be evaluated using classification accuracy.