DCTM：用于对话中多模态参与度估计的空洞卷积Transformer模型
DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation
AI总结：
本文提出DCTM空洞卷积Transformer模型，通过自注意力融合的简单拼接方法进行多模态特征融合，在MULTIMEDIATE 2023竞赛数据集上有效估计对话参与度，测试集性能提升7%。
摘要：
对话参与度估计被设定为回归问题，需要识别参与者在对话中的积极关注与投入。该任务对于深入理解人类在对话中的互动动态与行为模式至关重要。本研究引入空洞卷积Transformer，用于在MULTIMEDIATE 2023竞赛中对人类参与度进行建模与估计。我们提出的系统超越了基线模型，在测试集上提升了7%，在验证集上提升了4%。此外，我们采用不同模态融合机制，结果表明对于此类数据，采用自注意力融合的简单拼接方法能获得最佳性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
