论文标题
神经空间填充曲线
Neural Space-filling Curves
论文作者
论文摘要
我们提出了神经空间填充曲线（SFC），这是一种数据驱动的方法，用于推断一组图像的基于上下文的扫描顺序。像素的线性顺序构成了许多应用程序的基础，例如用于图像的生成建模中的视频扰流，压缩和自动回归模型。现有的算法诉诸固定扫描算法，例如栅格扫描或希尔伯特扫描。取而代之的是，我们的工作使用基于图的神经网络从图像数据集中学习了像素的空间连贯的线性顺序。当图像与扫描线顺序一起遍历时，对所得神经SFC进行了优化，适用于适合下游任务的目标。我们展示了在下游应用中使用神经SFC（例如图像压缩）的优势。代码和其他结果将在https://hywang66.github.io/publication/neuralsfc上提供。
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambling, compression, and auto-regressive models that are used in generative modeling for images. Existing algorithms resort to a fixed scanning algorithm such as Raster scan or Hilbert scan. Instead, our work learns a spatially coherent linear ordering of pixels from the dataset of images using a graph-based neural network. The resulting Neural SFC is optimized for an objective suitable for the downstream task when the image is traversed along with the scan line order. We show the advantage of using Neural SFCs in downstream applications such as image compression. Code and additional results will be made available at https://hywang66.github.io/publication/neuralsfc.
