Cross Attention Fusion

Cross-attention fusion is a technique that integrates information from multiple data sources (modalities) by leveraging attention mechanisms to selectively weigh the importance of features from each source. Current research focuses on applying this approach within various transformer-based architectures and other deep learning models to improve performance in diverse tasks, including image generation, remote sensing, and emotion recognition. This method's ability to effectively combine complementary information from different modalities leads to significant improvements in accuracy and robustness across a range of applications, impacting fields from medical imaging to human-computer interaction.