Capturing complex temporal relationships between video and audio modalities
is vital for Audio-Visual Emotion Recognition (AVER). However, existing methods
lack attention to local details, such as facial state changes between video
frames, which can reduce the discriminability of features and thus lower
recognition accuracy. In this paper, we propose a Detail-Enhanced Intra- and
Inter-modal Interaction network(DE-III) for AVER, incorporating several novel
aspects. We introduce optical flow information to enrich video representations
with texture details that better capture facial state changes. A fusion module
integrates the optical flow estimation with the corresponding video frames to
enhance the representation of facial texture variations. We also design
attentive intra- and inter-modal feature enhancement modules to further improve
the richness and discriminability of video and audio representations. A
detailed quantitative evaluation shows that our proposed model outperforms all
existing methods on three benchmark datasets for both concrete and continuous
emotion recognition. To encourage further research and ensure replicability, we
will release our full code upon acceptance.

我们提出了一个基于详细增强的模态内部和模态间交互网络（DE-III）的音频 - 视觉情感识别方法，利用光流信息丰富视频的纹理细节以捕捉面部状态变化，进一步改进视频和音频的丰富性和可区分性，通过详细的定量评估，我们的模型在三个基准数据集上都超过了现有方法，适用于具体和连续情感识别。