3 citations · 3 across the 4 of their papers we have counts for
6 papers
Attacking Motion Estimation with Adversarial Snow
Jenny Schmalfuss, Lukas Mehl, Andrés Bruhn
Current adversarial attacks for motion estimation (optical flow) optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, we exploit a r…
Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs
Jenny Schmalfuss, Erik Scheurer, Heng Zhao +3
Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of i…
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Hui Men, Vlad Hosu, Hanhe Lin +2
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predi…
Technical Report on Visual Quality Assessment for Frame Interpolation
Hui Men, Hanhe Lin, Vlad Hosu +3
Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpola…
ProFlow: Learning to Predict Optical Flow
Daniel Maurer, Andrés Bruhn
Temporal coherence is a valuable source of information in the context of optical flow estimation. However, finding a suitable motion model to leverage this information is a non-tri…
Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation
Yong Chul Ju, Daniel Maurer, Michael Breuß +1
Most of today's state-of-the-art methods for perspective shape from shading are modelled in terms of partial differential equations (PDEs) of Hamilton-Jacobi type. To improve the r…