21 citations · 51 across the 7 of their papers we have counts for
8 papers · 1 filter
TeST: Test-time Self-Training under Distribution Shift
Samarth Sinha, Peter Gehler, Francesco Locatello +1
Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter…
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations
Mohammadreza Zolfaghari, Yi Zhu, Peter Gehler +1
Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn c…
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…
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Mohamed Omran, Christoph Lassner, Gerard Pons-Moll +2
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is diffi…
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Sergey Prokudin, Peter Gehler, Sebastian Nowozin
Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging condit…
Semantic Video CNNs through Representation Warping
Raghudeep Gadde, Varun Jampani, Peter V. Gehler
In this work, we propose a technique to convert CNN models for semantic segmentation of static images into CNNs for video data. We describe a warping method that can be used to aug…