Tree Attention

Tree attention mechanisms are emerging as efficient alternatives to standard self-attention in various deep learning applications, primarily aiming to reduce the quadratic computational complexity associated with long sequences or large graphs. Current research focuses on developing tree-structured attention algorithms, such as those employed in Point Tree Transformers and DeFT, which prioritize relevant information through hierarchical processing and achieve linear or near-linear time complexity. These advancements are significantly impacting fields like natural language processing, computer vision (particularly point cloud registration), and graph neural networks, enabling faster and more scalable model inference for large-scale tasks.