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About this paper
Philipp Krähenbühl, Vladlen Koltun
Krähenbühl and Koltun introduced an efficient inference algorithm that makes fully connected pixel-level Conditional Random Fields computationally tractable for semantic image segmentation. The approach processes images in approximately 0.2 seconds, achieving 88.2% global accuracy on MSRC-21 by effectively modeling long-range pixel dependencies.
Key insights
- Message passing in mean field inference for fully connected CRFs can be efficiently formulated as convolution with Gaussian kernels.
- High-dimensional filtering using the permutohedral lattice enables the computation of these convolutions in linear time with respect to the number of pixels, rather than quadratic.
- Incorporating long-range dependencies through fully connected pairwise potentials significantly improves semantic image segmentation quality, especially at object boundaries.
Summary by alphaXiv.
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