4 papers
Probabilistic Pixel-Adaptive Refinement Networks
Anne S. Wannenwetsch, Stefan Roth
Encoder-decoder networks have found widespread use in various dense prediction tasks. However, the strong reduction of spatial resolution in the encoder leads to a loss of location…
Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice
Anne S. Wannenwetsch, Martin Kiefel, Peter V. Gehler +1
Dense prediction tasks typically employ encoder-decoder architectures, but the prevalent convolutions in the decoder are not image-adaptive and can lead to boundary artifacts. Diff…
Stochastic Variational Inference with Gradient Linearization
Tobias Plötz, Anne S. Wannenwetsch, Stefan Roth
Variational inference has experienced a recent surge in popularity owing to stochastic approaches, which have yielded practical tools for a wide range of model classes. A key benef…
ProbFlow: Joint Optical Flow and Uncertainty Estimation
Anne S. Wannenwetsch, Margret Keuper, Stefan Roth
Optical flow estimation remains challenging due to untextured areas, motion boundaries, occlusions, and more. Thus, the estimated flow is not equally reliable across the image. To…