  We show how to augment any convolutional network with an attention-based
global map to achieve non-local reasoning. We replace the final average pooling
by an attention-based aggregation layer akin to a single transformer block,
that weights how the patches are involved in the classification decision. We
plug this learned aggregation layer with a simplistic patch-based convolutional
network parametrized by 2 parameters (width and depth). In contrast with a
pyramidal design, this architecture family maintains the input patch resolution
across all the layers. It yields surprisingly competitive trade-offs between
accuracy and complexity, in particular in terms of memory consumption, as shown
by our experiments on various computer vision tasks: object classification,
image segmentation and detection.
