A Trustworthy Method for Multimodal Emotion Recognition
This addresses the need for reliable emotion recognition in applications like human-computer interaction, though it is incremental as it builds on existing multimodal methods by adding trustworthiness.
The paper tackles the problem of unreliable emotion recognition in noisy or out-of-distribution data by proposing a trustworthy method that uses uncertainty estimation to calculate prediction confidence, achieving state-of-the-art accuracy of 82.40% on Music-video and trusted F1 scores of 0.7511 and 0.9035 on IEMOCAP and Music-video.
Existing emotion recognition methods mainly focus on enhancing performance by employing complex deep models, typically resulting in significantly higher model complexity. Although effective, it is also crucial to ensure the reliability of the final decision, especially for noisy, corrupted and out-of-distribution data. To this end, we propose a novel emotion recognition method called trusted emotion recognition (TER), which utilizes uncertainty estimation to calculate the confidence value of predictions. TER combines the results from multiple modalities based on their confidence values to output the trusted predictions. We also provide a new evaluation criterion to assess the reliability of predictions. Specifically, we incorporate trusted precision and trusted recall to determine the trusted threshold and formulate the trusted Acc. and trusted F1 score to evaluate the model's trusted performance. The proposed framework combines the confidence module that accordingly endows the model with reliability and robustness against possible noise or corruption. The extensive experimental results validate the effectiveness of our proposed model. The TER achieves state-of-the-art performance on the Music-video, achieving 82.40% Acc. In terms of trusted performance, TER outperforms other methods on the IEMOCAP and Music-video, achieving trusted F1 scores of 0.7511 and 0.9035, respectively.