DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation

• URL: http://arxiv.org/abs/2509.17711v1
• Date: Mon, 22 Sep 2025 12:48:42 GMT
• Title: DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation
• Authors: Shenwei Kang, Xin Zhang, Wen Liu, Bin Li, Yujie Liu, Bo Gao,
• Abstract summary: DA-Mamba is a dialogue-aware multimodal architecture that replaces attention-heavy dialogue encoders with Mamba-based selective state-space processing.<n>We show that DA-Mamba surpasses prior state-of-the-art (SOTA) methods in concordance correlation coefficient (CCC)<n>These gains enable processing much longer sequences and facilitate real-time deployment in resource-constrained, multi-party conversational settings.
• Score: 15.106664911098882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human engagement estimation in conversational scenarios is essential for applications such as adaptive tutoring, remote healthcare assessment, and socially aware human--computer interaction. Engagement is a dynamic, multimodal signal conveyed by facial expressions, speech, gestures, and behavioral cues over time. In this work we introduce DA-Mamba, a dialogue-aware multimodal architecture that replaces attention-heavy dialogue encoders with Mamba-based selective state-space processing to achieve linear time and memory complexity while retaining expressive cross-modal reasoning. We design a Mamba dialogue-aware selective state-space model composed of three core modules: a Dialogue-Aware Encoder, and two Mamba-based fusion mechanisms: Modality-Group Fusion and Partner-Group Fusion, these modules achieve expressive dialogue understanding. Extensive experiments on three standard benchmarks (NoXi, NoXi-Add, and MPIIGI) show that DA-Mamba surpasses prior state-of-the-art (SOTA) methods in concordance correlation coefficient (CCC), while reducing training time and peak memory; these gains enable processing much longer sequences and facilitate real-time deployment in resource-constrained, multi-party conversational settings. The source code will be available at: https://github.com/kksssssss-ssda/MMEA.

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