mlsanityData to 14 Oct 2026

DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation

arXiv 2308.01966 ↗First posted 31 Jul 2023cs.MM cs.CL cs.LGcited by 3

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Abstract (arXiv)

Conversational engagement estimation is posed as a regression problem, entailing the identification of the favorable attention and involvement of the participants in the conversation. This task arises as a crucial pursuit to gain insights into human's interaction dynamics and behavior patterns within a conversation. In this research, we introduce a dilated convolutional Transformer for modeling and estimating human engagement in the MULTIMEDIATE 2023 competition. Our proposed system surpasses the baseline models, exhibiting a noteworthy 7% improvement on test set and 4% on validation set. Moreover, we employ different modality fusion mechanism and show that for this type of data, a simple concatenated method with self-attention fusion gains the best performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

19 of 43 references.

References · 19

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7 more
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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025PAEFF: Precise Alignment and Enhanced Gated Feature Fusion for Face-Voice Associationcited by 9
  2. 2025Interpretable Concept-based Deep Learning Framework for Multimodal Human Behavior Modelingcited by 1
  3. 2025Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classificationcited by 0