论文标题
SelectionConv：用于非线性图像数据的卷积神经网络
SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data
论文作者
论文摘要
卷积神经网络已彻底改变了视力应用。但是，有一些图像域和表示，不能通过标准CNN（例如球形图像，超像素）来处理。通常使用专门针对每种类型的网络和算法来处理此类数据。在这项工作中，我们表明可能并非总是有必要使用专门的神经网络在此类空间上操作。取而代之的是，我们介绍了一个新的结构化卷积操作员，该操作员可以复制2D卷积权重，将已经训练的传统CNN的功能转移到我们的新图形网络中。然后，该网络可以在任何可以表示为位置图的数据上运行。通过将非线性数据转换为图形，我们可以在这些不规则的图像域上应用这些卷积，而无需在大型域特异性数据集中训练。为各种此类数据形式展示了转移预训练的图像网络进行分割，风格化和深度预测的结果。
Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherical images, superpixels). Such data are usually processed using networks and algorithms specialized for each type. In this work, we show that it may not always be necessary to use specialized neural networks to operate on such spaces. Instead, we introduce a new structured graph convolution operator that can copy 2D convolution weights, transferring the capabilities of already trained traditional CNNs to our new graph network. This network can then operate on any data that can be represented as a positional graph. By converting non-rectilinear data to a graph, we can apply these convolutions on these irregular image domains without requiring training on large domain-specific datasets. Results of transferring pre-trained image networks for segmentation, stylization, and depth prediction are demonstrated for a variety of such data forms.
