  As the basic building block of Convolutional Neural Networks (CNNs), the
convolutional layer is designed to extract local patterns and lacks the ability
to model global context in its nature. Many efforts have been recently devoted
to complementing CNNs with the global modeling ability, especially by a family
of works on global feature interaction. In these works, the global context
information is incorporated into local features before they are fed into
convolutional layers. However, research on neuroscience reveals that the
neurons' ability of modifying their functions dynamically according to context
is essential for the perceptual tasks, which has been overlooked in most of
CNNs. Motivated by this, we propose one novel Context-Gated Convolution (CGC)
to explicitly modify the weights of convolutional layers adaptively under the
guidance of global context. As such, being aware of the global context, the
modulated convolution kernel of our proposed CGC can better extract
representative local patterns and compose discriminative features. Moreover,
our proposed CGC is lightweight and applicable with modern CNN architectures,
and consistently improves the performance of CNNs according to extensive
experiments on image classification, action recognition, and machine
translation. Our code of this paper is available at
https://github.com/XudongLinthu/context-gated-convolution.
