End-to-End Learning for Multimodal Emotion Recognition in Video With Adaptive Loss

This work presents an approach for emotion recognition in video through the interaction of visual, audio, and language information in an end-to-end learning manner with three key points: 1) lightweight feature extractor, 2) attention strategy, and 3) adaptive loss. We proposed a lightweight deep architecture with approximately 1 MB, which for the most crucial part, accounts for feature extraction, in the emotion recognition systems. The relationship in regard to the time dimension of features is explored with temporal convolutional network instead of RNNs-based architecture to leverage the parallelism and avoid the challenge of vanishing gradient. The attention strategy is employed to adjust the knowledge of temporal networks based on the time dimension and learning of each modality's contribution to the final results. The interaction between the modalities is also investigated when training with adaptive objective function, which adjusts the network's gradient. The experimental results obtained on a large-scale dataset for emotion recognition on Koreans demonstrate the superiority of our method when employing attention mechanism and adaptive loss during training.

Automated summary

This work introduces an approach for emotion recognition in videos by combining visual, audio, and language information, utilizing a lightweight feature extractor, attention strategy, and adaptive loss. The use of temporal convolutional network, attention mechanism, and adaptive loss during training significantly improves the performance in emotion recognition on a large dataset.