  Emotion Recognition in Conversations (ERC) is a popular task in natural
language processing, which aims to recognize the emotional state of the speaker
in conversations. While current research primarily emphasizes contextual
modeling, there exists a dearth of investigation into effective multimodal
fusion methods. We propose a novel framework called AIMDiT to solve the problem
of multimodal fusion of deep features. Specifically, we design a Modality
Augmentation Network which performs rich representation learning through
dimension transformation of different modalities and parameter-efficient
inception block. On the other hand, the Modality Interaction Network performs
interaction fusion of extracted inter-modal features and intra-modal features.
Experiments conducted using our AIMDiT framework on the public benchmark
dataset MELD reveal 2.34% and 2.87% improvements in terms of the Acc-7 and w-F1
metrics compared to the state-of-the-art (SOTA) models.
