Self Attention Block

Self-attention blocks are fundamental components of transformer-based architectures, enabling models to weigh the importance of different input elements when processing sequential data. Current research focuses on improving their efficiency and addressing limitations such as quadratic complexity with sequence length, exploring techniques like tree reductions, separable attention, and skip connections to reduce computational cost while maintaining or improving performance. These advancements are crucial for deploying transformers in resource-constrained environments and enhancing their capabilities in diverse applications, including image processing, natural language processing, and multi-modal learning.