GMConv: Modulating Effective Receptive Fields for Convolutional Kernels

In convolutional neural networks (CNNs), 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), which indicates the extent to 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). Specifically, GMConv utilizes the Gaussian function to generate a concentric symmetry mask that is placed over the kernel to refine the RF. We analyze the RFs of CNN kernels in different CNN layers and 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.