Semi-Supervised Multimodal Emotion Recognition with Class-Balanced Pseudo-labeling

This paper presents our solution for the Semi-Supervised Multimodal Emotion Recognition Challenge (MER2023-SEMI), addressing the issue of limited annotated data in emotion recognition. Recently, the self-training-based Semi-Supervised Learning∼(SSL) method has demonstrated its effectiveness in various tasks, including emotion recognition. However, previous studies focused on reducing the confirmation bias of data without adequately considering the issue of data imbalance, which is of great importance in emotion recognition. Additionally, previous methods have primarily focused on unimodal tasks and have not considered the inherent multimodal information in emotion recognition tasks. We propose a simple yet effective semi-supervised multimodal emotion recognition method to address the above issues. We assume that the pseudo-labeled samples with consistent results across unimodal and multimodal classifiers have a more negligible confirmation bias. Based on this assumption, we suggest using a class-balanced strategy to select top-k high-confidence pseudo-labeled samples from each class. The proposed method is validated to be effective on the MER2023-SEMI Grand Challenge, with the weighted F1 score reaching 88.53% on the test set.