Steerable Transformers for Volumetric Data

We introduce Steerable Transformers, an extension of the Vision Transformer that is equivariant to the action of the Special Euclidean group SE(d). We propose an steerable self-attention mechanism that operates on features extracted by steerable convolutions. Our experiments in both two and three dimensions show augmenting steerable convolutional networks with steerable transformer leads to improved performance.