Context-Gated Convolution

TL;DR

This paper introduces Context-Gated Convolution (CGC), a novel adaptive convolutional layer that dynamically modulates weights based on global context, enhancing CNN performance across various tasks.

Contribution

The paper proposes a lightweight, context-aware convolutional layer that adaptively modifies weights, inspired by neuroscience, to improve local feature extraction in CNNs.

Findings

Consistent performance improvements on image classification tasks.

Enhanced action recognition accuracy.

Better feature representation in machine translation.

Abstract

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.