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- W4313120895 abstract "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. Project page: https://hywang66.github.io/publication/neuralsfc ." @default.
- W4313120895 created "2023-01-06" @default.
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- W4313120895 date "2022-01-01" @default.
- W4313120895 modified "2023-09-23" @default.
- W4313120895 title "Neural Space-Filling Curves" @default.
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- W4313120895 doi "https://doi.org/10.1007/978-3-031-20071-7_25" @default.
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