Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention

TL;DR

This paper introduces an end-to-end multimodal model for group emotion recognition that fine-tunes all layers for improved accuracy, outperforming previous methods by approximately 8.5% on the VGAF dataset.

Contribution

The work presents a novel end-to-end training approach for multimodal emotion recognition, enabling joint optimization of feature extraction and fusion layers.

Findings

Achieved 60.37% validation accuracy, outperforming baseline by 8.5%.

Fine-tuning all layers improves model performance.

Effective integration of audio and visual modalities for emotion recognition.

Abstract

Classifying group-level emotions is a challenging task due to complexity of video, in which not only visual, but also audio information should be taken into consideration. Existing works on multimodal emotion recognition are using bulky approach, where pretrained neural networks are used as a feature extractors and then extracted features are being fused. However, this approach does not consider attributes of multimodal data and feature extractors cannot be fine-tuned for specific task which can be disadvantageous for overall model accuracy. To this end, our impact is twofold: (i) we train model end-to-end, which allows early layers of neural network to be adapted with taking into account later, fusion layers, of two modalities; (ii) all layers of our model was fine-tuned for downstream task of emotion recognition, so there were no need to train neural networks from scratch. Our model achieves best validation accuracy of 60.37% which is approximately 8.5% higher, than VGAF dataset baseline and is competitive with existing works, audio and video modalities.