Convolution is one of the basic building blocks of CNN architectures. Despite
its common use, standard convolution has two main shortcomings:
Content-agnostic and Computation-heavy. Dynamic filters are content-adaptive,
while further increasing the computational overhead. Depth-wise convolution is
a lightweight variant, but it usually leads to a drop in CNN performance or
requires a larger number of channels. In this work, we propose the Decoupled
Dynamic Filter (DDF) that can simultaneously tackle both of these shortcomings.
Inspired by recent advances in attention, DDF decouples a depth-wise dynamic
filter into spatial and channel dynamic filters. This decomposition
considerably reduces the number of parameters and limits computational costs to
the same level as depth-wise convolution. Meanwhile, we observe a significant
boost in performance when replacing standard convolution with DDF in
classification networks. ResNet50 / 101 get improved by 1.9% and 1.3% on the
top-1 accuracy, while their computational costs are reduced by nearly half.
Experiments on the detection and joint upsampling networks also demonstrate the
superior performance of the DDF upsampling variant (DDF-Up) in comparison with
standard convolution and specialized content-adaptive layers.

提出了一种 Decoupled Dynamic Filter（DDF）卷积方法，可以同时解决标准卷积的两个主要缺点：内容不适配和计算量大。DDF 将深度动态滤波器分解为空间动态滤波器和通道动态滤波器，大大减少了参数数量，并将计算成本限制在与深度卷积相同的水平上。在分类网络中，使用 DDF 替换标准卷积可以显著提高性能，同时其计算成本几乎减半。在检测和联合上采样网络上进行的实验也证明了 DDF-upsampling 变体在比标准卷积和专门的内容自适应层更优秀的性能。