  Recently, some approaches are proposed to harness deep convolutional networks
to facilitate superpixel segmentation. The common practice is to first evenly
divide the image into a pre-defined number of grids and then learn to associate
each pixel with its surrounding grids. However, simply applying a series of
convolution operations with limited receptive fields can only implicitly
perceive the relations between the pixel and its surrounding grids.
Consequently, existing methods often fail to provide an effective context when
inferring the association map. To remedy this issue, we propose a novel
\textbf{A}ssociation \textbf{I}mplantation (AI) module to enable the network to
explicitly capture the relations between the pixel and its surrounding grids.
The proposed AI module directly implants the features of grid cells to the
surrounding of its corresponding central pixel, and conducts convolution on the
padded window to adaptively transfer knowledge between them. With such an
implantation operation, the network could explicitly harvest the pixel-grid
level context, which is more in line with the target of superpixel segmentation
comparing to the pixel-wise relation. Furthermore, to pursue better boundary
precision, we design a boundary-perceiving loss to help the network
discriminate the pixels around boundaries in hidden feature level, which could
benefit the subsequent inferring modules to accurately identify more boundary
pixels. Extensive experiments on BSDS500 and NYUv2 datasets show that our
method could not only achieve state-of-the-art performance but maintain
satisfactory inference efficiency.
