  Interactive single-image segmentation is ubiquitous in the scientific and
commercial imaging software. In this work, we focus on the single-image
segmentation problem only with some seeds such as scribbles. Inspired by the
dynamic receptive field in the human being's visual system, we propose the
Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the
contextual information for neural networks. The core idea is randomly selecting
the spatial sampling area according to the Gaussian distribution offsets. Our
GDC can be easily used as a module to build lightweight or complex segmentation
networks. We adopt the proposed GDC to address the typical single-image
segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid
Pooling to show its potential and generality in common semantic segmentation.
Experiments demonstrate that the GDC outperforms other existing convolutions on
three benchmark segmentation datasets including Pascal-Context, Pascal-VOC
2012, and Cityscapes. Additional experiments are also conducted to illustrate
that the GDC can produce richer and more vivid features compared with other
convolutions. In general, our GDC is conducive to the convolutional neural
networks to form an overall impression of the image.
