  Attention mechanisms, and most prominently self-attention, are a powerful
building block for processing not only text but also images. These provide a
parameter efficient method for aggregating inputs. We focus on self-attention
in vision models, and we combine it with convolution, which as far as we know,
are the first to do. What emerges is a convolution with data dependent filters.
We call this an Affine Self Convolution. While this is applied differently at
each spatial location, we show that it is translation equivariant. We also
modify the Squeeze and Excitation variant of attention, extending both variants
of attention to the roto-translation group. We evaluate these new models on
CIFAR10 and CIFAR100 and show an improvement in the number of parameters, while
reaching comparable or higher accuracy at test time against self-trained
baselines.
