Multi-modal intent detection aims to utilize various modalities to understand the user's intentions, which is essential for the deployment of dialogue systems in real-world scenarios. The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data. In this work, we introduce a shallow-to-deep interaction framework with data augmentation (SDIF-DA) to address the above challenges. Firstly, SDIF-DA leverages a shallow-to-deep interaction module to progressively and effectively align and fuse features across text, video, and audio modalities. Secondly, we propose a ChatGPT-based data augmentation approach to automatically augment sufficient training data. Experimental results demonstrate that SDIF-DA can effectively align and fuse multi-modal features by achieving state-of-the-art performance. In addition, extensive analyses show that the introduced data augmentation approach can successfully distill knowledge from the large language model.

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