  As a variant of standard convolution, a dilated convolution can control
effective receptive fields and handle large scale variance of objects without
introducing additional computational costs. To fully explore the potential of
dilated convolution, we proposed a new type of dilated convolution (referred to
as inception convolution), where the convolution operations have independent
dilation patterns among different axes, channels and layers. To develop a
practical method for learning complex inception convolution based on the data,
a simple but effective search algorithm, referred to as efficient dilation
optimization (EDO), is developed. Based on statistical optimization, the EDO
method operates in a low-cost manner and is extremely fast when it is applied
on large scale datasets. Empirical results validate that our method achieves
consistent performance gains for image recognition, object detection, instance
segmentation, human detection, and human pose estimation. For instance, by
simply replacing the 3x3 standard convolution in the ResNet-50 backbone with
inception convolution, we significantly improve the AP of Faster R-CNN from
36.4% to 39.2% on MS COCO.
