Self Attention

Self-attention is a mechanism in neural networks that allows the model to weigh the importance of different parts of the input data when processing it, enabling the capture of long-range dependencies and contextual information. Current research focuses on improving the efficiency of self-attention, particularly in vision transformers and other large models, through techniques like low-rank approximations, selective attention, and grouped query attention, aiming to reduce computational costs while maintaining accuracy. These advancements are significantly impacting various fields, including computer vision, natural language processing, and time series analysis, by enabling more efficient and powerful models for tasks such as image restoration, text-to-image generation, and medical image segmentation.