Multimodal Conversational Emotion (MCE) detection, generally spanning across
the acoustic, vision and language modalities, has attracted increasing interest
in the multimedia community. Previous studies predominantly focus on learning
contextual information in conversations with only a few considering the topic
information in single language modality, while always neglecting the acoustic
and vision topic information. On this basis, we propose a model-agnostic
Topic-enriched Diffusion (TopicDiff) approach for capturing multimodal topic
information in MCE tasks. Particularly, we integrate the diffusion model into
neural topic model to alleviate the diversity deficiency problem of neural
topic model in capturing topic information. Detailed evaluations demonstrate
the significant improvements of TopicDiff over the state-of-the-art MCE
baselines, justifying the importance of multimodal topic information to MCE and
the effectiveness of TopicDiff in capturing such information. Furthermore, we
observe an interesting finding that the topic information in acoustic and
vision is more discriminative and robust compared to the language.

基于视觉、语言和声学等多模态，我们提出了一种面向多模态对话情感检测任务的主题丰富扩散（TopicDiff）方法，通过将扩散模型与神经主题模型相结合，解决了神经主题模型在捕获主题信息时存在的多样性缺失问题，并通过详细评估证明了 TopicDiff 在多模态情感检测任务中相对于最先进的基线方法的显著性改进，验证了多模态主题信息在此任务中的重要性以及 TopicDiff 捕获该信息的有效性。此外，我们还观察到声学和视觉主题信息相对于语言主题信息更具有区分性和鲁棒性的有趣发现。