DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation

• URL: http://arxiv.org/abs/2410.08470v1
• Date: Fri, 11 Oct 2024 02:43:45 GMT
• Title: DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation
• Authors: Jia Li, Yangchen Yu, Yin Chen, Yu Zhang, Peng Jia, Yunbo Xu, Ziqiang Li, Meng Wang, Richang Hong,
• Abstract summary: Engagement estimation plays a crucial role in understanding human social behaviors. We propose a Dialogue-Aware Transformer framework that relies solely on audio-visual input and is language-independent. Our approach achieves a CCC score of 0.76 on the NoXi Base test set and an average CCC of 0.64 across the NoXi Base, NoXi-Add, and MPIIGI test sets.
• Score: 42.87704953679693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-computer interaction. In this paper, we propose a Dialogue-Aware Transformer framework (DAT) with Modality-Group Fusion (MGF), which relies solely on audio-visual input and is language-independent, for estimating human engagement in conversations. Specifically, our method employs a modality-group fusion strategy that independently fuses audio and visual features within each modality for each person before inferring the entire audio-visual content. This strategy significantly enhances the model's performance and robustness. Additionally, to better estimate the target participant's engagement levels, the introduced Dialogue-Aware Transformer considers both the participant's behavior and cues from their conversational partners. Our method was rigorously tested in the Multi-Domain Engagement Estimation Challenge held by MultiMediate'24, demonstrating notable improvements in engagement-level regression precision over the baseline model. Notably, our approach achieves a CCC score of 0.76 on the NoXi Base test set and an average CCC of 0.64 across the NoXi Base, NoXi-Add, and MPIIGI test sets.

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