Masked Attention

Masked attention is a technique that selectively focuses on relevant parts of input data, improving efficiency and interpretability in various machine learning models. Current research focuses on applying masked attention within transformer architectures, particularly for image and video processing tasks like segmentation, object detection, and change detection, often incorporating it into novel models such as Mask2Former. This approach enhances model performance by reducing computational cost and improving the accuracy and clinical meaningfulness of results, leading to advancements in diverse fields including medical imaging, remote sensing, and autonomous driving.