  As a powerful engine, vanilla convolution has promoted huge breakthroughs in
various computer tasks. However, it often suffers from sample and content
agnostic problems, which limits the representation capacities of the
convolutional neural networks (CNNs). In this paper, we for the first time
model the scene features as a combination of the local spatial-adaptive parts
owned by the individual and the global shift-invariant parts shared to all
individuals, and then propose a novel two-branch dual complementary dynamic
convolution (DCDC) operator to flexibly deal with these two types of features.
The DCDC operator overcomes the limitations of vanilla convolution and most
existing dynamic convolutions who capture only spatial-adaptive features, and
thus markedly boosts the representation capacities of CNNs. Experiments show
that the DCDC operator based ResNets (DCDC-ResNets) significantly outperform
vanilla ResNets and most state-of-the-art dynamic convolutional networks on
image classification, as well as downstream tasks including object detection,
instance and panoptic segmentation tasks, while with lower FLOPs and
parameters.
