# MultiMediate'24: Multi-Domain Engagement Estimation

arXiv: https://arxiv.org/abs/2408.16625 · HTML: https://mlsanity.com/paper/2408.16625/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2024.

## Built on by (most cited first, 6 of 6)

- [DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation](https://mlsanity.com/paper/2410.08470/) (2024)
- [Beyond Words: Multimodal LLM Knows When to Speak](https://mlsanity.com/paper/2505.14654/) (2025)
- [Boosting Team Modeling through Tempo-Relational Representation Learning](https://mlsanity.com/paper/2507.13305/) (2025)
- [Generalizable Engagement Estimation in Conversation via Domain Prompting and Parallel Attention](https://mlsanity.com/paper/2508.14448/) (2025)
- [DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation](https://mlsanity.com/paper/2509.17711/) (2025)
- [From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams](https://mlsanity.com/paper/2608.23254/) (2026)

## Abstract (arXiv)

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.