  In convolutional neural networks, the convolutions are conventionally
performed using a square kernel with a fixed N $\times$ N receptive field (RF).
However, what matters most to the network is the effective receptive field
(ERF) that indicates the extent with which input pixels contribute to an output
pixel. Inspired by the property that ERFs typically exhibit a Gaussian
distribution, we propose a Gaussian Mask convolutional kernel (GMConv) in this
work. Specifically, GMConv utilizes the Gaussian function to generate a
concentric symmetry mask that is placed over the kernel to refine the RF. Our
GMConv can directly replace the standard convolutions in existing CNNs and can
be easily trained end-to-end by standard back-propagation. We evaluate our
approach through extensive experiments on image classification and object
detection tasks. Over several tasks and standard base models, our approach
compares favorably against the standard convolution. For instance, using GMConv
for AlexNet and ResNet-50, the top-1 accuracy on ImageNet classification is
boosted by 0.98% and 0.85%, respectively.
