  Convolutional neural networks excel in a number of computer vision tasks. One
of their most crucial architectural elements is the effective receptive field
size, that has to be manually set to accommodate a specific task. Standard
solutions involve large kernels, down/up-sampling and dilated convolutions.
These require testing a variety of dilation and down/up-sampling factors and
result in non-compact representations and excessive number of parameters. We
address this issue by proposing a new convolution filter composed of displaced
aggregation units (DAU). DAUs learn spatial displacements and adapt the
receptive field sizes of individual convolution filters to a given problem,
thus eliminating the need for hand-crafted modifications. DAUs provide a
seamless substitution of convolutional filters in existing state-of-the-art
architectures, which we demonstrate on AlexNet, ResNet50, ResNet101, DeepLab
and SRN-DeblurNet. The benefits of this design are demonstrated on a variety of
computer vision tasks and datasets, such as image classification (ILSVRC 2012),
semantic segmentation (PASCAL VOC 2011, Cityscape) and blind image de-blurring
(GOPRO). Results show that DAUs efficiently allocate parameters resulting in up
to four times more compact networks at similar or better performance.
