Emotion recognition technology plays a crucial role in human-computer interaction and affective computing applications, with potential applications spanning from mental health monitoring to educational technology. However, the majority of existing emotion recognition systems suffer from significant performance degradation when deployed across different environments and datasets, limiting their practical applicability. This highlights the critical need for robust cross-domain solutions that can maintain performance consistency across varied real-world conditions. With the advancement of multimodal learning and domain adaptation techniques, attention-based fusion models have shown promising results in emotion recognition tasks. However, cross-domain emotion recognition still faces substantial challenges due to domain distribution shifts, limited computational resources, and the need to balance source and target domain performance.