  We propose a new convolution called Dynamic Region-Aware Convolution
(DRConv), which can automatically assign multiple filters to corresponding
spatial regions where features have similar representation. In this way, DRConv
outperforms standard convolution in modeling semantic variations. Standard
convolutional layer can increase the number of filers to extract more visual
elements but results in high computational cost. More gracefully, our DRConv
transfers the increasing channel-wise filters to spatial dimension with
learnable instructor, which not only improve representation ability of
convolution, but also maintains computational cost and the
translation-invariance as standard convolution dose. DRConv is an effective and
elegant method for handling complex and variable spatial information
distribution. It can substitute standard convolution in any existing networks
for its plug-and-play property, especially to power convolution layers in
efficient networks. We evaluate DRConv on a wide range of models (MobileNet
series, ShuffleNetV2, etc.) and tasks (Classification, Face Recognition,
Detection and Segmentation). On ImageNet classification, DRConv-based
ShuffleNetV2-0.5x achieves state-of-the-art performance of 67.1% at 46M
multiply-adds level with 6.3% relative improvement.
