Products of Edge-perts. - Peter V. Gehler; Max Welling (NIPS, 2005)

The rate adapting poisson model for information retrieval and object recognition. - Peter V. Gehler; Alex Holub; Max Welling (ICML, 2006)

Deterministic Annealing for Multiple-Instance Learning. - Peter V. Gehler; Olivier Chapelle (AISTATS, 2007)

Bayesian color constancy revisited. - Peter V. Gehler; Carsten Rother; Andrew Blake 001; Thomas P. Minka; Toby Sharp (CVPR, 2008)

Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers. - Peter V. Gehler; Sebastian Nowozin (CVPR, 2009)

On feature combination for multiclass object classification. - Peter V. Gehler; Sebastian Nowozin (ICCV, 2009)

Kernel learning approaches for image classification. - Peter Vincent Gehler (2009)

Scene Carving: Scene Consistent Image Retargeting. - Alex Mansfield; Peter V. Gehler; Luc Van Gool; Carsten Rother (ECCV (1), 2010)

Visibility Maps for Improving Seam Carving. - Alex Mansfield; Peter V. Gehler; Luc Van Gool; Carsten Rother (ECCV Workshops (1), 2010)

On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation. - Sebastian Nowozin; Peter V. Gehler; Christoph H. Lampert (ECCV (6), 2010)

Branch&Rank: Non-Linear Object Detection. - Alain D. Lehmann; Peter V. Gehler; Luc Van Gool (BMVC, 2011)

Learning Output Kernels with Block Coordinate Descent. - Francesco Dinuzzo; Cheng Soon Ong; Peter V. Gehler; Gianluigi Pillonetto (ICML, 2011)

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance. - Peter V. Gehler; Carsten Rother; Martin Kiefel; Lumin Zhang; Bernhard Schölkopf (NIPS, 2011)

Teaching 3D geometry to deformable part models. - Bojan Pepik; Michael Stark 003; Peter V. Gehler; Bernt Schiele (CVPR, 2012)

Pottics - The Potts Topic Model for Semantic Image Segmentation. - Christoph Dann; Peter V. Gehler; Stefan Roth 001; Sebastian Nowozin (DAGM/OAGM Symposium, 2012)

3D2PM - 3D Deformable Part Models. - Bojan Pepik; Peter V. Gehler; Michael Stark 003; Bernt Schiele (ECCV (6), 2012)

Occlusion Patterns for Object Class Detection. - Bojan Pepik; Michael Stark 003; Peter V. Gehler; Bernt Schiele (CVPR, 2013)

Poselet Conditioned Pictorial Structures. - Leonid Pishchulin; Mykhaylo Andriluka; Peter V. Gehler; Bernt Schiele (CVPR, 2013)

A Non-parametric Bayesian Network Prior of Human Pose. - Andreas M. Lehrmann; Peter V. Gehler; Sebastian Nowozin (ICCV, 2013)

Strong Appearance and Expressive Spatial Models for Human Pose Estimation. - Leonid Pishchulin; Mykhaylo Andriluka; Peter V. Gehler; Bernt Schiele (ICCV, 2013)

2D Human Pose Estimation: New Benchmark and State of the Art Analysis. - Mykhaylo Andriluka; Leonid Pishchulin; Peter V. Gehler; Bernt Schiele (CVPR, 2014)

Efficient Nonlinear Markov Models for Human Motion. - Andreas M. Lehrmann; Peter V. Gehler; Sebastian Nowozin (CVPR, 2014)

Human Pose Estimation with Fields of Parts. - Martin Kiefel; Peter Vincent Gehler (ECCV (5), 2014)

Intrinsic Video. - Naejin Kong; Peter V. Gehler; Michael J. Black (ECCV (2), 2014)

Multi-View Priors for Learning Detectors from Sparse Viewpoint Data. - Bojan Pepik; Michael Stark 003; Peter V. Gehler; Bernt Schiele (ICLR (Poster), 2014)

Branch&Rank for Efficient Object Detection. - Alain D. Lehmann; Peter V. Gehler; Luc Van Gool (Int. J. Comput. Vis., 2014)

3D object class detection in the wild. - Bojan Pepik; Michael Stark 003; Peter V. Gehler; Tobias Ritschel 001; Bernt Schiele (CVPR Workshops, 2015)

Efficient Facade Segmentation Using Auto-context. - Varun Jampani; Raghudeep Gadde; Peter V. Gehler (WACV, 2015)

Superpixel Convolutional Networks using Bilateral Inceptions. - Raghudeep Gadde; Varun Jampani; Martin Kiefel; Peter V. Gehler (CoRR, 2015)

Permutohedral Lattice CNNs. - Martin Kiefel; Varun Jampani; Peter V. Gehler (ICLR (Workshop), 2015)

Sparse Convolutional Networks using the Permutohedral Lattice. - Martin Kiefel; Varun Jampani; Peter V. Gehler (CoRR, 2015)

The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models. - Varun Jampani; Sebastian Nowozin; Matthew Loper; Peter V. Gehler (Comput. Vis. Image Underst., 2015)

Multi-View and 3D Deformable Part Models. - Bojan Pepik; Michael Stark 003; Peter V. Gehler; Bernt Schiele (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks. - Varun Jampani; Martin Kiefel; Peter V. Gehler (CVPR, 2016)

DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation. - Leonid Pishchulin; Eldar Insafutdinov; Siyu Tang 001; Bjoern Andres; Mykhaylo Andriluka; Peter V. Gehler; Bernt Schiele (CVPR, 2016)

Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. - Federica Bogo; Angjoo Kanazawa; Christoph Lassner; Peter V. Gehler; Javier Romero 002; Michael J. Black (ECCV (5), 2016)

Superpixel Convolutional Networks Using Bilateral Inceptions. - Raghudeep Gadde; Varun Jampani; Martin Kiefel; Daniel Kappler; Peter V. Gehler (ECCV (1), 2016)

Barrista: Caffe Well-Served. - Christoph Lassner; Daniel Kappler; Martin Kiefel; Peter V. Gehler (ACM Multimedia, 2016)

Video Propagation Networks. - Varun Jampani; Raghudeep Gadde; Peter V. Gehler (CVPR, 2017)

Unite the People: Closing the Loop Between 3D and 2D Human Representations. - Christoph Lassner; Javier Romero 002; Martin Kiefel; Federica Bogo; Michael J. Black; Peter V. Gehler (CVPR, 2017)

Reflectance Adaptive Filtering Improves Intrinsic Image Estimation. - Thomas Nestmeyer; Peter V. Gehler (CVPR, 2017)

Learning to Filter Object Detections. - Sergey Prokudin; Daniel Kappler; Sebastian Nowozin; Peter V. Gehler (GCPR, 2017)

Semantic Video CNNs Through Representation Warping. - Raghudeep Gadde; Varun Jampani; Peter V. Gehler (ICCV, 2017)

A Generative Model of People in Clothing. - Christoph Lassner; Gerard Pons-Moll; Peter V. Gehler (ICCV, 2017)

A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation. - Graham D. Finlayson; Ghalia Hemrit; Arjan Gijsenij; Peter V. Gehler (CIC, 2017)

Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation. - Mohamed Omran; Christoph Lassner; Gerard Pons-Moll; Peter V. Gehler; Bernt Schiele (3DV, 2018)

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification. - Sergey Prokudin; Peter V. Gehler; Sebastian Nowozin (ECCV (9), 2018)

Rehabilitating the ColorChecker Dataset for Illuminant Estimation. - Ghalia Hemrit; Graham D. Finlayson; Arjan Gijsenij; Peter V. Gehler; Simone Bianco 001; Brian V. Funt; Mark S. Drew; Lilong Shi (CIC, 2018)

Rehabilitating the Color Checker Dataset for Illuminant Estimation. - Ghalia Hemrit; Graham D. Finlayson; Arjan Gijsenij; Peter V. Gehler; Simone Bianco 001; Mark S. Drew (CoRR, 2018)

Efficient 2D and 3D Facade Segmentation Using Auto-Context. - Raghudeep Gadde; Varun Jampani; Renaud Marlet; Peter V. Gehler (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice. - Anne S. Wannenwetsch; Martin Kiefel; Peter V. Gehler; Stefan Roth 001 (GCPR, 2019)

Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo. - Stepan Tulyakov; François Fleuret; Martin Kiefel; Peter V. Gehler; Michael Hirsch (ICCV, 2019)

Towards causal generative scene models via competition of experts. - Julius von Kügelgen; Ivan Ustyuzhaninov; Peter V. Gehler; Matthias Bethge; Bernhard Schölkopf (CoRR, 2020)

Providing a Single Ground-Truth for Illuminant Estimation for the ColorChecker Dataset. - Ghalia Hemrit; Graham D. Finlayson; Arjan Gijsenij; Peter V. Gehler; Simone Bianco 001; Mark S. Drew; Brian V. Funt; Lilong Shi (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations. - Mohammadreza Zolfaghari; Yi Zhu; Peter V. Gehler; Thomas Brox (ICCV, 2021)

Dynamic Inference with Neural Interpreters. - Nasim Rahaman; Muhammad Waleed Gondal; Shruti Joshi; Peter V. Gehler; Yoshua Bengio; Francesco Locatello; Bernhard Schölkopf (NeurIPS, 2021)

Backward-Compatible Prediction Updates: A Probabilistic Approach. - Frederik Träuble; Julius von Kügelgen; Matthäus Kleindessner; Francesco Locatello; Bernhard Schölkopf; Peter V. Gehler (NeurIPS, 2021)

Adapting ImageNet-scale models to complex distribution shifts with self-learning. - Evgenia Rusak; Steffen Schneider 001; Peter V. Gehler; Oliver Bringmann 001; Wieland Brendel; Matthias Bethge (CoRR, 2021)

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. - Andrea Dittadi; Frederik Träuble; Manuel Wüthrich; Felix Widmaier; Peter V. Gehler; Ole Winther; Francesco Locatello; Olivier Bachem; Bernhard Schölkopf; Stefan Bauer (CoRR, 2021)

Towards Total Recall in Industrial Anomaly Detection. - Karsten Roth; Latha Pemula; Joaquin Zepeda; Bernhard Schölkopf; Thomas Brox; Peter V. Gehler (CVPR, 2022)

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. - Osama Makansi; Julius von Kügelgen; Francesco Locatello; Peter Vincent Gehler; Dominik Janzing; Thomas Brox; Bernhard Schölkopf (ICLR, 2022)

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. - Lukas Schott; Julius von Kügelgen; Frederik Träuble; Peter Vincent Gehler; Chris Russell 001; Matthias Bethge; Bernhard Schölkopf; Francesco Locatello; Wieland Brendel (ICLR, 2022)

The Role of Pretrained Representations for the OOD Generalization of RL Agents. - Frederik Träuble; Andrea Dittadi; Manuel Wuthrich; Felix Widmaier; Peter Vincent Gehler; Ole Winther; Francesco Locatello; Olivier Bachem; Bernhard Schölkopf; Stefan Bauer (ICLR, 2022)

Assaying Out-Of-Distribution Generalization in Transfer Learning. - Florian Wenzel; Andrea Dittadi; Peter V. Gehler; Carl-Johann Simon-Gabriel; Max Horn; Dominik Zietlow; David Kernert; Chris Russell 001; Thomas Brox; Bernt Schiele; Bernhard Schölkopf; Francesco Locatello (NeurIPS, 2022)

If your data distribution shifts, use self-learning. - Evgenia Rusak; Steffen Schneider 001; George Pachitariu; Luisa Eck; Peter Vincent Gehler; Oliver Bringmann 001; Wieland Brendel; Matthias Bethge (Trans. Mach. Learn. Res., 2022)

Unsupervised Object Learning via Common Fate. - Matthias Tangemann; Steffen Schneider 001; Julius von Kügelgen; Francesco Locatello; Peter Vincent Gehler; Thomas Brox; Matthias Kümmerer; Matthias Bethge; Bernhard Schölkopf (CLeaR, 2023)

TeST: Test-time Self-Training under Distribution Shift. - Samarth Sinha; Peter V. Gehler; Francesco Locatello; Bernt Schiele (WACV, 2023)