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Found 4 papers, 2 papers with code
Probabilistic Pixel-Adaptive Refinement Networks
Encoder-decoder networks have found widespread use in various dense prediction tasks.
Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice
When adding our network layer to state-of-the-art networks for optical flow and semantic segmentation, boundary artifacts are removed and the accuracy is improved.
Stochastic Variational Inference with Gradient Linearization
In this paper we propose stochastic variational inference with gradient linearization (SVIGL).
ProbFlow: Joint Optical Flow and Uncertainty Estimation
We overcome the artificial separation of optical flow and confidence estimation by introducing a method that jointly predicts optical flow and its underlying uncertainty.
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