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.

本文提出了一种新的关联植入（AI）模块，用于增强深度卷积神经网络的超像素分割能力，该模块直接将网格单元的特征植入其相应中心像素的周围，并在填充窗口上进行卷积以自适应地在它们之间转移知识，通过此种植入操作，网络可以明确地获取像素-网格级别的上下文信息。为了追求更好的边界精度，本文还设计了一个边界感知的损失函数，辅助网络在隐藏特征层面上区分边界周围的像素，从而将更多边界像素准确地识别出来。在BSDS500和NYUv2数据集上的广泛实验表明，我们的方法不仅能够达到最先进的性能，而且能保持令人满意的推理效率。

AINet: 超像素分割的关联植入