Manifold Attention

Manifold attention leverages the geometric properties of data, represented as points on curved manifolds rather than in flat Euclidean space, to improve attention mechanisms in deep learning models. Current research focuses on incorporating manifold attention into various architectures, including transformers and networks processing data like EEG signals and point clouds, often using Riemannian geometry and self-attention mechanisms to capture complex relationships within the data. This approach enhances the ability of models to handle non-Euclidean data structures and noisy or heterogeneous datasets, leading to improved performance in applications such as place recognition, brain disease diagnosis, and 3D point cloud classification.