Axial Attention

Axial attention is a computational approach that efficiently processes data by applying attention mechanisms along individual axes (e.g., rows and columns) before combining the results, improving upon the computational cost of standard self-attention. Current research focuses on integrating axial attention into various architectures, including convolutional neural networks and transformers, for tasks such as medical image segmentation, high-frequency trading, and 3D human pose estimation. This technique offers advantages in handling long-range dependencies and multi-scale information within data, leading to improved performance and efficiency in diverse applications across computer vision and signal processing.