  Transformer attention architectures, similar to those developed for natural
language processing, have recently proved efficient also in vision, either in
conjunction with or as a replacement for convolutional layers. Typically,
visual attention is inserted in the network architecture as a (series of)
feedforward self-attention module(s), with mutual key-query agreement as the
main selection and routing operation. However efficient, this strategy is only
vaguely compatible with the way that attention is implemented in biological
brains: as a separate and unified network of attentional selection regions,
receiving inputs from and exerting modulatory influence on the entire hierarchy
of visual regions. Here, we report experiments with a simple such attention
system that can improve the performance of standard convolutional networks,
with relatively few additional parameters. Each spatial position in each layer
of the network produces a key-query vector pair; all queries are then pooled
into a global attention query. On the next iteration, the match between each
key and the global attention query modulates the network's activations --
emphasizing or silencing the locations that agree or disagree (respectively)
with the global attention system. We demonstrate the usefulness of this
brain-inspired Global Attention Agreement network (GAttANet) for various
convolutional backbones (from a simple 5-layer toy model to a standard ResNet50
architecture) and datasets (CIFAR10, CIFAR100, Imagenet-1k). Each time, our
global attention system improves accuracy over the corresponding baseline.
