Dual Attention

Dual attention mechanisms in machine learning aim to improve model performance by selectively focusing on both local and global features within data. Current research emphasizes the integration of dual attention with various architectures, including transformers, convolutional neural networks, and graph neural networks, to enhance tasks such as image processing, time series analysis, and multimodal data fusion. This approach has yielded significant improvements in accuracy and efficiency across diverse applications, from medical image analysis and driver distraction detection to anomaly detection in video and improved large language model inference. The widespread adoption of dual attention reflects its effectiveness in capturing complex relationships within data, leading to more robust and informative models.