  Dilation convolution is a critical mutant of standard convolution neural
network to control effective receptive fields and handle large scale variance
of objects without introducing additional computation. However, fitting the
effective reception field to data with dilated convolution is less discussed in
the literature. To fully explore its potentials, we proposed a new mutant of
dilated convolution, namely inception (dilated) convolution where the
convolutions have independent dilation among different axes, channels and
layers. To explore a practical method for fitting the complex inception
convolution to the data, a simple while effective dilation search
algorithm(EDO) based on statistical optimization is developed. The search
method operates in a zero-cost manner which is extremely fast to apply on large
scale datasets. Empirical results reveal that our method obtains consistent
performance gains in an extensive range of benchmarks. For instance, by simply
replace the 3 x 3 standard convolutions in ResNet-50 backbone with inception
convolution, we improve the mAP of Faster-RCNN on MS-COCO from 36.4% to 39.2%.
Furthermore, using the same replacement in ResNet-101 backbone, we achieve a
huge improvement over AP score from 60.2% to 68.5% on COCO val2017 for the
bottom up human pose estimation.
