  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.
