  In this paper, we propose Global Context Convolutional Network (GCCN) for
visual recognition. GCCN computes global features representing contextual
information across image patches. These global contextual features are defined
as local maxima pixels with high visual sharpness in each patch. These features
are then concatenated and utilised to augment the convolutional features. The
learnt feature vector is normalised using the global context features using
Frobenius norm. This straightforward approach achieves high accuracy in
compassion to the state-of-the-art methods with 94.6% and 95.41% on CIFAR-10
and STL-10 datasets, respectively. To explore potential impact of GCCN on other
visual representation tasks, we implemented GCCN as a based model to few-shot
image classification. We learn metric distances between the augmented feature
vectors and their prototypes representations, similar to Prototypical and
Matching Networks. GCCN outperforms state-of-the-art few-shot learning methods
achieving 99.9%, 84.8% and 80.74% on Omniglot, MiniImageNet and CUB-200,
respectively. GCCN has significantly improved on the accuracy of
state-of-the-art prototypical and matching networks by up to 30% in different
few-shot learning scenarios.
