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ORCIDPeter V. Gehler
Peter Vincent Gehler
Person information
- affiliation: University of Tübingen, Bernstein Center for Computational Neuroscience, Germany
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation: ETH Zurich, Computer Vision Laboratory, Switzerland
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Books and Theses
- 2009
[b1]Peter Vincent Gehler:
Kernel learning approaches for image classification. Saarland University, 2009
Journal Articles
- 2022
- [j6]Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge:
If your data distribution shifts, use self-learning. Trans. Mach. Learn. Res. 2022 (2022) - 2020
- [j5]Ghalia Hemrit
, Graham D. Finlayson, Arjan Gijsenij, Peter V. Gehler, Simone Bianco, Mark S. Drew, Brian V. Funt, Lilong Shi:
Providing a Single Ground-Truth for Illuminant Estimation for the ColorChecker Dataset. IEEE Trans. Pattern Anal. Mach. Intell. 42(5): 1286-1287 (2020) - 2018
- [j4]Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter V. Gehler:
Efficient 2D and 3D Facade Segmentation Using Auto-Context. IEEE Trans. Pattern Anal. Mach. Intell. 40(5): 1273-1280 (2018) - 2015
- [j3]Varun Jampani, Sebastian Nowozin, Matthew Loper, Peter V. Gehler:
The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models. Comput. Vis. Image Underst. 136: 32-44 (2015) - [j2]Bojan Pepik, Michael Stark, Peter V. Gehler, Bernt Schiele:
Multi-View and 3D Deformable Part Models. IEEE Trans. Pattern Anal. Mach. Intell. 37(11): 2232-2245 (2015) - 2014
- [j1]Alain D. Lehmann, Peter V. Gehler, Luc Van Gool:
Branch&Rank for Efficient Object Detection. Int. J. Comput. Vis. 106(3): 252-268 (2014)
Conference and Workshop Papers
- 2023
- [c54]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CLeaR 2023: 281-327 - [c53]Samarth Sinha, Peter V. Gehler, Francesco Locatello, Bernt Schiele:
TeST: Test-time Self-Training under Distribution Shift. WACV 2023: 2758-2768 - 2022
- [c52]Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter V. Gehler:
Towards Total Recall in Industrial Anomaly Detection. CVPR 2022: 14298-14308 - [c51]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR 2022 - [c50]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR 2022 - [c49]Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
The Role of Pretrained Representations for the OOD Generalization of RL Agents. ICLR 2022 - [c48]Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. NeurIPS 2022 - 2021
- [c47]Mohammadreza Zolfaghari, Yi Zhu, Peter V. Gehler, Thomas Brox:
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations. ICCV 2021: 1430-1439 - [c46]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS 2021: 116-128 - [c45]Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf:
Dynamic Inference with Neural Interpreters. NeurIPS 2021: 10985-10998 - 2019
- [c44]Anne S. Wannenwetsch, Martin Kiefel, Peter V. Gehler, Stefan Roth:
Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice. GCPR 2019: 345-359 - [c43]Stepan Tulyakov, François Fleuret, Martin Kiefel, Peter V. Gehler, Michael Hirsch:
Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo. ICCV 2019: 1527-1537 - 2018
- [c42]Mohamed Omran, Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler, Bernt Schiele:
Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation. 3DV 2018: 484-494 - [c41]Sergey Prokudin, Peter V. Gehler, Sebastian Nowozin:
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification. ECCV (9) 2018: 542-559 - [c40]Ghalia Hemrit, Graham D. Finlayson, Arjan Gijsenij, Peter V. Gehler, Simone Bianco, Brian V. Funt, Mark S. Drew, Lilong Shi:
Rehabilitating the ColorChecker Dataset for Illuminant Estimation. CIC 2018: 350-353 - 2017
- [c39]Thomas Nestmeyer, Peter V. Gehler:
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation. CVPR 2017: 1771-1780 - [c38]Varun Jampani, Raghudeep Gadde, Peter V. Gehler:
Video Propagation Networks. CVPR 2017: 3154-3164 - [c37]Christoph Lassner, Javier Romero, Martin Kiefel, Federica Bogo, Michael J. Black, Peter V. Gehler:
Unite the People: Closing the Loop Between 3D and 2D Human Representations. CVPR 2017: 4704-4713 - [c36]Sergey Prokudin, Daniel Kappler, Sebastian Nowozin, Peter V. Gehler:
Learning to Filter Object Detections. GCPR 2017: 52-62 - [c35]Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler:
A Generative Model of People in Clothing. ICCV 2017: 853-862 - [c34]Raghudeep Gadde, Varun Jampani, Peter V. Gehler:
Semantic Video CNNs Through Representation Warping. ICCV 2017: 4463-4472 - [c33]Graham D. Finlayson, Ghalia Hemrit, Arjan Gijsenij, Peter V. Gehler:
A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation. CIC 2017: 64-69 - 2016
- [c32]Varun Jampani, Martin Kiefel, Peter V. Gehler:
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks. CVPR 2016: 4452-4461 - [c31]Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter V. Gehler, Bernt Schiele:
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation. CVPR 2016: 4929-4937 - [c30]Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter V. Gehler, Javier Romero, Michael J. Black:
Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. ECCV (5) 2016: 561-578 - [c29]Raghudeep Gadde, Varun Jampani, Martin Kiefel, Daniel Kappler, Peter V. Gehler:
Superpixel Convolutional Networks Using Bilateral Inceptions. ECCV (1) 2016: 597-613 - [c28]Christoph Lassner, Daniel Kappler, Martin Kiefel, Peter V. Gehler:
Barrista: Caffe Well-Served. ACM Multimedia 2016: 1210-1213 - 2015
- [c27]Bojan Pepik, Michael Stark, Peter V. Gehler, Tobias Ritschel, Bernt Schiele:
3D object class detection in the wild. CVPR Workshops 2015: 1-10 - [c26]Varun Jampani, Raghudeep Gadde, Peter V. Gehler:
Efficient Facade Segmentation Using Auto-context. WACV 2015: 1038-1045 - [c25]Martin Kiefel, Varun Jampani, Peter V. Gehler:
Permutohedral Lattice CNNs. ICLR (Workshop) 2015 - 2014
- [c24]Andreas M. Lehrmann, Peter V. Gehler, Sebastian Nowozin:
Efficient Nonlinear Markov Models for Human Motion. CVPR 2014: 1314-1321 - [c23]Mykhaylo Andriluka, Leonid Pishchulin, Peter V. Gehler, Bernt Schiele:
2D Human Pose Estimation: New Benchmark and State of the Art Analysis. CVPR 2014: 3686-3693 - [c22]Martin Kiefel, Peter Vincent Gehler:
Human Pose Estimation with Fields of Parts. ECCV (5) 2014: 331-346 - [c21]Naejin Kong, Peter V. Gehler, Michael J. Black:
Intrinsic Video. ECCV (2) 2014: 360-375 - [c20]Bojan Pepik, Michael Stark, Peter V. Gehler, Bernt Schiele:
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data. ICLR (Poster) 2014 - 2013
- [c19]Leonid Pishchulin, Mykhaylo Andriluka, Peter V. Gehler, Bernt Schiele:
Poselet Conditioned Pictorial Structures. CVPR 2013: 588-595 - [c18]Bojan Pepik, Michael Stark, Peter V. Gehler, Bernt Schiele:
Occlusion Patterns for Object Class Detection. CVPR 2013: 3286-3293 - [c17]Andreas M. Lehrmann, Peter V. Gehler, Sebastian Nowozin:
A Non-parametric Bayesian Network Prior of Human Pose. ICCV 2013: 1281-1288 - [c16]Leonid Pishchulin, Mykhaylo Andriluka, Peter V. Gehler, Bernt Schiele:
Strong Appearance and Expressive Spatial Models for Human Pose Estimation. ICCV 2013: 3487-3494 - 2012
- [c15]Bojan Pepik, Michael Stark, Peter V. Gehler, Bernt Schiele:
Teaching 3D geometry to deformable part models. CVPR 2012: 3362-3369 - [c14]Christoph Dann, Peter V. Gehler, Stefan Roth, Sebastian Nowozin:
Pottics - The Potts Topic Model for Semantic Image Segmentation. DAGM/OAGM Symposium 2012: 397-407 - [c13]Bojan Pepik, Peter V. Gehler, Michael Stark, Bernt Schiele:
3D2PM - 3D Deformable Part Models. ECCV (6) 2012: 356-370 - 2011
- [c12]Alain D. Lehmann, Peter V. Gehler, Luc Van Gool:
Branch&Rank: Non-Linear Object Detection. BMVC 2011: 1-11 - [c11]Francesco Dinuzzo, Cheng Soon Ong, Peter V. Gehler, Gianluigi Pillonetto:
Learning Output Kernels with Block Coordinate Descent. ICML 2011: 49-56 - [c10]Peter V. Gehler, Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf:
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance. NIPS 2011: 765-773 - 2010
- [c9]Sebastian Nowozin, Peter V. Gehler, Christoph H. Lampert:
On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation. ECCV (6) 2010: 98-111 - [c8]Alex Mansfield, Peter V. Gehler, Luc Van Gool, Carsten Rother:
Visibility Maps for Improving Seam Carving. ECCV Workshops (1) 2010: 131-144 - [c7]Alex Mansfield, Peter V. Gehler, Luc Van Gool, Carsten Rother:
Scene Carving: Scene Consistent Image Retargeting. ECCV (1) 2010: 143-156 - 2009
- [c6]Peter V. Gehler, Sebastian Nowozin:
Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers. CVPR 2009: 2836-2843 - [c5]Peter V. Gehler, Sebastian Nowozin:
On feature combination for multiclass object classification. ICCV 2009: 221-228 - 2008
- [c4]Peter V. Gehler, Carsten Rother, Andrew Blake, Thomas P. Minka, Toby Sharp:
Bayesian color constancy revisited. CVPR 2008 - 2007
- [c3]Peter V. Gehler, Olivier Chapelle:
Deterministic Annealing for Multiple-Instance Learning. AISTATS 2007: 123-130 - 2006
- [c2]Peter V. Gehler, Alex Holub, Max Welling:
The rate adapting poisson model for information retrieval and object recognition. ICML 2006: 337-344 - 2005
- [c1]Peter V. Gehler, Max Welling:
Products of Edge-perts. NIPS 2005: 419-426
Editorship
- 2015
- [e1]Juergen Gall, Peter V. Gehler, Bastian Leibe:
Pattern Recognition - 37th German Conference, GCPR 2015, Aachen, Germany, October 7-10, 2015, Proceedings. Lecture Notes in Computer Science 9358, Springer 2015, ISBN 978-3-319-24946-9 [contents]
Informal and Other Publications
- 2022
- [i28]Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. CoRR abs/2207.09239 (2022) - [i27]Samarth Sinha, Peter V. Gehler, Francesco Locatello, Bernt Schiele:
TeST: Test-time Self-Training under Distribution Shift. CoRR abs/2209.11459 (2022) - 2021
- [i26]Evgenia Rusak, Steffen Schneider, Peter V. Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge:
Adapting ImageNet-scale models to complex distribution shifts with self-learning. CoRR abs/2104.12928 (2021) - [i25]Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter V. Gehler:
Towards Total Recall in Industrial Anomaly Detection. CoRR abs/2106.08265 (2021) - [i24]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR abs/2107.01057 (2021) - [i23]Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter V. Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. CoRR abs/2107.05686 (2021) - [i22]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR abs/2107.08221 (2021) - [i21]Mohammadreza Zolfaghari, Yi Zhu, Peter V. Gehler, Thomas Brox:
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations. CoRR abs/2109.14910 (2021) - [i20]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. CoRR abs/2110.05304 (2021) - [i19]Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf:
Dynamic Inference with Neural Interpreters. CoRR abs/2110.06399 (2021) - [i18]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CoRR abs/2110.06562 (2021) - 2020
- [i17]Julius von Kügelgen, Ivan Ustyuzhaninov, Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf:
Towards causal generative scene models via competition of experts. CoRR abs/2004.12906 (2020) - 2019
- [i16]Anne S. Wannenwetsch, Martin Kiefel, Peter V. Gehler, Stefan Roth:
Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice. CoRR abs/1909.03677 (2019) - 2018
- [i15]Sergey Prokudin, Peter V. Gehler, Sebastian Nowozin:
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification. CoRR abs/1805.03430 (2018) - [i14]Ghalia Hemrit, Graham D. Finlayson, Arjan Gijsenij, Peter V. Gehler, Simone Bianco, Mark S. Drew:
Rehabilitating the Color Checker Dataset for Illuminant Estimation. CoRR abs/1805.12262 (2018) - [i13]Mohamed Omran, Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler, Bernt Schiele:
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation. CoRR abs/1808.05942 (2018) - 2017
- [i12]Christoph Lassner, Javier Romero, Martin Kiefel, Federica Bogo, Michael J. Black, Peter V. Gehler:
Unite the People: Closing the Loop Between 3D and 2D Human Representations. CoRR abs/1701.02468 (2017) - [i11]Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler:
A Generative Model of People in Clothing. CoRR abs/1705.04098 (2017) - [i10]Raghudeep Gadde, Varun Jampani, Peter V. Gehler:
Semantic Video CNNs through Representation Warping. CoRR abs/1708.03088 (2017) - 2016
- [i9]Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter V. Gehler:
Efficient 2D and 3D Facade Segmentation using Auto-Context. CoRR abs/1606.06437 (2016) - [i8]Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter V. Gehler, Javier Romero, Michael J. Black:
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. CoRR abs/1607.08128 (2016) - [i7]Thomas Nestmeyer, Peter V. Gehler:
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation. CoRR abs/1612.05062 (2016) - [i6]Varun Jampani, Raghudeep Gadde, Peter V. Gehler:
Video Propagation Networks. CoRR abs/1612.05478 (2016) - 2015
- [i5]Martin Kiefel, Varun Jampani, Peter V. Gehler:
Sparse Convolutional Networks using the Permutohedral Lattice. CoRR abs/1503.04949 (2015) - [i4]Bojan Pepik, Michael Stark, Peter V. Gehler, Tobias Ritschel, Bernt Schiele:
3D Object Class Detection in the Wild. CoRR abs/1503.05038 (2015) - [i3]Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter V. Gehler, Bernt Schiele:
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation. CoRR abs/1511.06645 (2015) - [i2]Raghudeep Gadde, Varun Jampani, Martin Kiefel, Peter V. Gehler:
Superpixel Convolutional Networks using Bilateral Inceptions. CoRR abs/1511.06739 (2015) - 2014
- [i1]Varun Jampani, Sebastian Nowozin, Matthew Loper, Peter V. Gehler:
The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models. CoRR abs/1402.0859 (2014)
Coauthor Index
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