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.

提出了一种名为 DRConv 的动态区域感知卷积，它可以自动将多个过滤器分配给具有相似表示的相应空间区域，以更有效处理复杂和不同的空间信息分布，从而代替任何现有网络中的标准卷积，尤其适用于高效网络中的动力卷积层。在 ImageNet 分类中，DRConv-based ShuffleNetV2-0.5x 在 46M 乘积添加水平下实现了 67.1% 的最新性能，相对改进了 6.3%。