Multimodal Fusion Method with Spatiotemporal Sequences and Relationship Learning for Valence-Arousal Estimation
This work addresses emotion recognition for affective computing applications, but it is incremental as it combines existing techniques like TCN and Transformer in a multimodal fusion approach.
The paper tackled the problem of estimating valence-arousal from multimodal video and audio data by developing a model that integrates pre-trained backbones, Temporal Convolutional Networks for spatiotemporal encoding, and a Transformer for long-range dependencies, achieving competitive performance on the AffWild2 dataset.
This paper presents our approach for the VA (Valence-Arousal) estimation task in the ABAW6 competition. We devised a comprehensive model by preprocessing video frames and audio segments to extract visual and audio features. Through the utilization of Temporal Convolutional Network (TCN) modules, we effectively captured the temporal and spatial correlations between these features. Subsequently, we employed a Transformer encoder structure to learn long-range dependencies, thereby enhancing the model's performance and generalization ability. Our method leverages a multimodal data fusion approach, integrating pre-trained audio and video backbones for feature extraction, followed by TCN-based spatiotemporal encoding and Transformer-based temporal information capture. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance in VA estimation on the AffWild2 dataset.