We propose contextual convolution (CoConv) for visual recognition. CoConv is
a direct replacement of the standard convolution, which is the core component
of convolutional neural networks. CoConv is implicitly equipped with the
capability of incorporating contextual information while maintaining a similar
number of parameters and computational cost compared to the standard
convolution. CoConv is inspired by neuroscience studies indicating that (i)
neurons, even from the primary visual cortex (V1 area), are involved in
detection of contextual cues and that (ii) the activity of a visual neuron can
be influenced by the stimuli placed entirely outside of its theoretical
receptive field. On the one hand, we integrate CoConv in the widely-used
residual networks and show improved recognition performance over baselines on
the core tasks and benchmarks for visual recognition, namely image
classification on the ImageNet data set and object detection on the MS COCO
data set. On the other hand, we introduce CoConv in the generator of a
state-of-the-art Generative Adversarial Network, showing improved generative
results on CIFAR-10 and CelebA. Our code is available at
this https URL

本文介绍了一种用于视觉识别的上下文卷积技术，理论基础来自神经科学研究；在图像分类和对象检测任务以及生成式对抗网络的生成结果方面，该技术都取得了较好的性能。