Multi-Label Multimodal Emotion Recognition With Transformer-Based Fusion and Emotion-Level Representation Learning.

Emotion recognition has been an active research area for a long time. Recently, multimodal emotion recognition from video data has grown in importance with the explosion of video content due to the emergence of short video social media platforms. Effectively incorporating information from multiple modalities in video data to learn robust multimodal representation for improving recognition model performance is still the primary challenge for researchers. In this context, transformer architectures have been widely used and have significantly improved multimodal deep learning and representation learning. Inspired by this, we propose a transformer-based fusion and representation learning method to fuse and enrich multimodal features from raw videos for the task of multi-label video emotion recognition. Specifically, our method takes raw video frames, audio signals, and text subtitles as inputs and passes information from these multiple modalities through a unified transformer architecture for learning a joint multimodal representation. Moreover, we use the label-level representation approach to deal with the multi-label classification task and enhance the model performance. We conduct experiments on two benchmark datasets: Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Carnegie Mellon University Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) to evaluate our proposed method. The experimental results demonstrate that the proposed method outperforms other strong baselines and existing approaches for multi-label video emotion recognition.