Scalable Attention

Scalable attention mechanisms aim to overcome the computational limitations of standard attention models, which exhibit quadratic complexity with sequence length, hindering their application to long sequences common in various domains like document translation and medical image analysis. Current research focuses on developing efficient attention architectures, including linear attention and variations of transformers, often incorporating techniques like superpixel segmentation or convolutional blocks to improve performance and scalability. These advancements enable the application of attention-based deep learning models to larger datasets and more complex tasks, leading to improvements in areas such as medical image segmentation, machine translation, and movement quality assessment.