论文标题
超越固定网格：学习几何图像表示具有可变形的网格
Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid
论文作者
论文摘要
在现代计算机视觉中，图像通常表示为固定的统一网格，并大步向前，并通过深度卷积神经网络进行处理。我们认为，将网格更好地与高频图像内容保持一致是一种更有效的策略。我们介绍了\ emph {可变形的网格} Defgrid，这是一个可学习的神经网络模块，可预测二维三角网格的顶点的位置偏移，从而使变形网格的边缘与图像边界保持一致。我们在各种用例中展示了我们的偏格，即通过将其插入各种处理级别的模块。我们将Defgrid用作端到端\ emph {可学习的几何降采样}层，该层代替了标准的池化方法，用于减少图像中的特征分辨率。与在语义分割任务上使用CNN相比，我们在相同的网格分辨率下显示出显着改善的结果。我们还利用在输出层的偏格来完成对象掩盖注释的任务，并证明对我们预测的多边形网格上对象边界的推理会导致对现有基于像素和曲线的方法的更准确的结果。我们最终将Defgrid作为无监督图像分区的独立模块展示，显示出优于现有方法的性能。项目网站：http：//www.cs.toronto.edu/~jungao/def-grid
In modern computer vision, images are typically represented as a fixed uniform grid with some stride and processed via a deep convolutional neural network. We argue that deforming the grid to better align with the high-frequency image content is a more effective strategy. We introduce \emph{Deformable Grid} DefGrid, a learnable neural network module that predicts location offsets of vertices of a 2-dimensional triangular grid, such that the edges of the deformed grid align with image boundaries. We showcase our DefGrid in a variety of use cases, i.e., by inserting it as a module at various levels of processing. We utilize DefGrid as an end-to-end \emph{learnable geometric downsampling} layer that replaces standard pooling methods for reducing feature resolution when feeding images into a deep CNN. We show significantly improved results at the same grid resolution compared to using CNNs on uniform grids for the task of semantic segmentation. We also utilize DefGrid at the output layers for the task of object mask annotation, and show that reasoning about object boundaries on our predicted polygonal grid leads to more accurate results over existing pixel-wise and curve-based approaches. We finally showcase DefGrid as a standalone module for unsupervised image partitioning, showing superior performance over existing approaches. Project website: http://www.cs.toronto.edu/~jungao/def-grid
