MultiMediate 2024

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

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. Engagement is closely linked to the previous MultiMediate tasks of eye contact- backchannel detection. For the purpose of this challenge, we collected novel annotations of engagement on the Novice-Expert Interaction (NoXi) database (Cafaro et al., 2017). 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). 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. For the multi-domain engagement estimation task we only provide pre-computed featuresets due to privacy constraints on some of the test data.

The overall performance of a team will be evaluated by taking the average CCC across three different test datasets. Of these three datasets, two include validation sets that are made available to challenge participants:

NOXI (MultiMediate’23 version): This part of the evaluation set is identical to the test set of MultiMediate'23 and consists of 16 sessions (in English, French and German). That is, the MultiMediate'23 version of the NOXI test set comes from the same domain as the training set, providing a reference to compare MultiMediate'24 submissions to MultiMediate'23 results, as well as a point of comparison for evaluating the impact of out-of-domain test scenarios on performance.
NOXI (additional languages): This evaluation set includes four languages that are not part of the NOXI training set: 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.
MPIIGroupInteraction: For MultiMediate'24 we collected novel 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.

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.

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.

Evaluation of Participants’ Approaches

Training, validation, and test data for each sub-challenge can be downloaded at multimediate-challenge.org/Datasets/. We provide pre-computed features to minimise the overhead for participants. For the tasks newly included in this years’ challenge, the test set is now released. Participants can submit their predictions for evaluation at https://hcai.eu/challenges/web/challenges/challenge-page/23/overview.

We will evaluate approaches with the following metrics: accuracy for backchannel detection and eye contact estimation, mean squared error for agreement estimation from backchannels, and next speaker prediction is evaluated with unweighted average recall.

Rules for participation

The competition is team-based. A single person can only be part of a single team.
For bodily behaviour recognition and engagement estimation tasks, each team will have 5 evaluation runs on the test set (per task).
For the tasks that were already included in Multimediate’21-23, three evaluations on the test set are allowed per month. In July 2024, we will make an exception and allow for five evaluations on the test set.
Additional datasets can be used, but they need to be publicly available.
The Organisers will not participate in the challenge.
The evaluation servers will be open until 12 July 2024.
The test set (without labels) will be provided to participants 2 weeks before the challenge deadline. It is not allowed to manually annotate the test set.

Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation

Michal Balazia, Philipp Müller, Ákos Levente Tánczos, August von Liechtenstein, François Brémond

Proceedings of the 30th ACM International Conference on Multimedia, pp. 70–79, 2022.

Abstract Links BibTeX

Body language is an eye-catching social signal and its automatic analysis can significantly advance artificial intelligence systems to understand and actively participate in social interactions. While computer vision has made impressive progress in low-level tasks like head and body pose estimation, the detection of more subtle behaviors such as gesturing, grooming, or fumbling is not well explored. In this paper we present BBSI, the first set of annotations of complex Bodily Behaviors embedded in continuous Social Interactions in a group setting. Based on previous work in psychology, we manually annotated 26 hours of spontaneous human behavior in the MPIIGroupInteraction dataset with 15 distinct body language classes. We present comprehensive descriptive statistics on the resulting dataset as well as results of annotation quality evaluations. For automatic detection of these behaviors, we adapt the Pyramid Dilated Attention Network (PDAN), a state-of-the-art approach for human action detection. We perform experiments using four variants of spatial-temporal features as input to PDAN: Two-Stream Inflated 3D CNN, Temporal Segment Networks, Temporal Shift Module and Swin Transformer. Results are promising and indicate a great room for improvement in this difficult task. Representing a key piece in the puzzle towards automatic understanding of social behavior, BBSI is fully available to the research community.

doi: 10.1145/3503161.3548363

Paper: balazia22_mm.pdf

Paper Access: https://doi.org/10.1145/3503161.3548363

@inproceedings{balazia22_mm, author = {Balazia, Michal and M\"{u}ller, Philipp and T\'{a}nczos, \'{A}kos Levente and Liechtenstein, August von and Br\'{e}mond, Fran\c{c}ois}, title = {Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation}, year = {2022}, url = {https://doi.org/10.1145/3503161.3548363}, doi = {10.1145/3503161.3548363}, booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, pages = {70–79} }

The NoXi Database: Multimodal Recordings of Mediated Novice-Expert Interactions

Angelo Cafaro, Johannes Wagner, Tobias Baur, Soumia Dermouche, Mercedes Torres Torres, Catherine Pelachaud, Elisabeth André, Michel Valstar

Proceedings of 19th ACM International Conference on Multimodal Interaction, pp. 350–359, 2017.

Abstract Links BibTeX

We present a novel multi-lingual database of natural dyadic novice- expert interactions, named NoXi, featuring screen-mediated dyadic human interactions in the context of information exchange and retrieval. NoXi is designed to provide spontaneous interactions with emphasis on adaptive behaviors and unexpected situations (e.g. conversational interruptions). A rich set of audio-visual data, as well as continuous and discrete annotations are publicly available through a web interface. Descriptors include low level social signals (e.g. gestures, smiles), functional descriptors (e.g. turn-taking, dialogue acts) and interaction descriptors (e.g. engagement, interest, and fluidity).

doi: 10.1145/3136755.3136780

Paper: cafaro17_icmi.pdf

@inproceedings{cafaro17_icmi, title = {The NoXi Database: Multimodal Recordings of Mediated Novice-Expert Interactions}, author = {Cafaro, Angelo and Wagner, Johannes and Baur, Tobias and Dermouche, Soumia and Torres, Mercedes Torres and Pelachaud, Catherine and André, Elisabeth and Valstar, Michel}, year = {2017}, booktitle = {Proceedings of 19th ACM International Conference on Multimodal Interaction}, doi = {10.1145/3136755.3136780}, pages = {350–359} }