Peter Gehler

发表

The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models

Sebastian Nowozin, Peter V. Gehler, Varun Jampani, 2014, Comput. Vis. Image Underst..

Kernel learning approaches for image classification

Peter V. Gehler, Peter Gehler, 2009 .

Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers

Peter V. Gehler, Sebastian Nowozin, S. Nowozin, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

An introduction to kernel learning algorithms

Peter V. Gehler, Bernhard Schölkopf, B. Schölkopf, 2009 .

Structured Prediction for Event Detection

Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, 2014 .

AD3: A Fast Decoder for Structured Prediction

Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, 2014 .

Generalized Sequential Tree-Reweighted Message Passing

Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, 2014 .

Structured Prediction for Object Boundary Detection in Images

Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, 2014 .

TeST: Test-time Self-Training under Distribution Shift

B. Schiele, Samarth Sinha, Francesco Locatello, 2022, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Assaying Out-Of-Distribution Generalization in Transfer Learning

B. Schiele, B. Schölkopf, T. Brox, 2022, NeurIPS.

ImageNet-D: A new challenging robustness dataset inspired by domain adaptation

M. Bethge, Wieland Brendel, O. Bringmann, 2022 .

If your data distribution shifts, use self-learning

M. Bethge, Wieland Brendel, O. Bringmann, 2021, Trans. Mach. Learn. Res..

1 The Power of LP Relaxation for MAP Inference

Christoph H. Lampert, S. Nowozin, P. Gehler, 2013 .

Implicit Wiener Series, Part II: Regularised estimation

P. Gehler, M. Franz, Peter Gehler, 2006 .

TR-178 Infinite Kernel Learning

S. Nowozin, P. Gehler, Peter Gehler, 2008 .

Smoothed Coordinate Descent for MAP Inference

Christoph H. Lampert, S. Nowozin, P. Gehler, 2014 .

The rate adapting poisson model for information retrieval and object recognition

Peter V. Gehler, Max Welling, Alex Holub, 2006, ICML.

Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

Peter V. Gehler, Michael J. Black, Javier Romero, 2016, ECCV.

Sparse Convolutional Networks using the Permutohedral Lattice pdf

Peter V. Gehler, Varun Jampani, Martin Kiefel, 2015, ArXiv.

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

Julius von Kügelgen, M. Bethge, B. Schölkopf, 2021, ICLR.

Intrinsic Video

Peter V. Gehler, Michael J. Black, Naejin Kong, 2014, ECCV.

2D Human Pose Estimation: New Benchmark and State of the Art Analysis

Bernt Schiele, Mykhaylo Andriluka, Leonid Pishchulin, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

On feature combination for multiclass object classification

Sebastian Nowozin, Peter V. Gehler, S. Nowozin, 2009, 2009 IEEE 12th International Conference on Computer Vision.

An introduction to graphical models

P. Gehler, Peter Gehler, 2001 .

Efficient Nonlinear Markov Models for Human Motion

Sebastian Nowozin, Peter V. Gehler, Andreas M. Lehrmann, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Advanced Structured Prediction

Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, 2014 .

Dynamic Inference with Neural Interpreters pdf

Yoshua Bengio, Bernhard Scholkopf, Muhammad Waleed Gondal, 2021, NeurIPS.

DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

Peter V. Gehler, Bernt Schiele, Bjoern Andres, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Poselet Conditioned Pictorial Structures

Peter V. Gehler, Bernt Schiele, Mykhaylo Andriluka, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Teaching 3D geometry to deformable part models

Peter V. Gehler, Bernt Schiele, Bojan Pepik, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Bayesian color constancy revisited

Andrew Blake, Peter V. Gehler, Toby Sharp, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation

Peter V. Gehler, Graham D. Finlayson, Ghalia Hemrit, 2017, CIC.

Towards Total Recall in Industrial Anomaly Detection

B. Schölkopf, T. Brox, P. Gehler, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Superpixel Convolutional Networks Using Bilateral Inceptions

Peter V. Gehler, Varun Jampani, Daniel Kappler, 2015, ECCV.

Unite the People: Closing the Loop Between 3D and 2D Human Representations

Peter V. Gehler, Michael J. Black, Javier Romero, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation

Peter V. Gehler, Bernt Schiele, Mohamed Omran, 2018, 2018 International Conference on 3D Vision (3DV).

Strong Appearance and Expressive Spatial Models for Human Pose Estimation

Peter V. Gehler, Bernt Schiele, Mykhaylo Andriluka, 2013, 2013 IEEE International Conference on Computer Vision.

Backward-Compatible Prediction Updates: A Probabilistic Approach pdf

Peter V. Gehler, Bernhard Schölkopf, Matthäus Kleindessner, 2021, NeurIPS.

CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations pdf

Thomas Brox, Peter Gehler, Mohammadreza Zolfaghari, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Learning Output Kernels with Block Coordinate Descent

Peter V. Gehler, Cheng Soon Ong, Gianluigi Pillonetto, 2011, ICML.

Multi-View and 3D Deformable Part Models

Peter V. Gehler, Bernt Schiele, Bojan Pepik, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

3D object class detection in the wild

Peter V. Gehler, Bernt Schiele, Tobias Ritschel, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

3D2PM - 3D Deformable Part Models

Peter V. Gehler, Bernt Schiele, Bojan Pepik, 2012, ECCV.

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction pdf

Thomas Brox, Osama Makansi, Dominik Janzing, 2021, ICLR.

Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo

Peter V. Gehler, Michael Hirsch, Martin Kiefel, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Visibility Maps for Improving Seam Carving

Luc Van Gool, Peter V. Gehler, Carsten Rother, 2010, ECCV Workshops.

Scene Carving: Scene Consistent Image Retargeting

Luc Van Gool, Peter V. Gehler, Carsten Rother, 2010, ECCV.

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Peter V. Gehler, Bernhard Schölkopf, Carsten Rother, 2011, NIPS.

Unsupervised Object Learning via Common Fate

Julius von Kügelgen, M. Bethge, B. Schölkopf, 2021, CLeaR.

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Sebastian Nowozin, Peter V. Gehler, Sergey Prokudin, 2018, ECCV.

Video Propagation Networks

Peter V. Gehler, Varun Jampani, Raghudeep Gadde, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

Peter V. Gehler, Varun Jampani, Martin Kiefel, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Semantic Video CNNs Through Representation Warping

Peter V. Gehler, Varun Jampani, Raghudeep Gadde, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Towards causal generative scene models via competition of experts pdf

Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf, 2020, ArXiv.

A Generative Model of People in Clothing

Peter V. Gehler, Christoph Lassner, Gerard Pons-Moll, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Occlusion Patterns for Object Class Detection

Peter V. Gehler, Bernt Schiele, Bojan Pepik, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Learning to Filter Object Detections

Sebastian Nowozin, Peter V. Gehler, Daniel Kappler, 2017, GCPR.

Deterministic Annealing for Multiple-Instance Learning

Peter V. Gehler, Olivier Chapelle, O. Chapelle, 2007, AISTATS.

Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Peter V. Gehler, Thomas Nestmeyer, Peter Gehler, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice pdf

Peter V. Gehler, Anne S. Wannenwetsch, Stefan Roth, 2019, GCPR.

On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation

Sebastian Nowozin, Peter V. Gehler, Christoph H. Lampert, 2010, ECCV.

Adapting ImageNet-scale models to complex distribution shifts with self-learning

M. Bethge, Wieland Brendel, O. Bringmann, 2021, ArXiv.

The Role of Pretrained Representations for the OOD Generalization of RL Agents

O. Winther, F. Widmaier, Stefan Bauer, 2021, ICLR.

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning pdf

Ole Winther, Peter V. Gehler, Bernhard Schölkopf, 2021, ArXiv.

U NSUPERVISED O BJECT L EARNING VIA C OMMON F ATE pdf

Thomas Brox, Matthias Bethge, Bernhard Scholkopf, 2021 .

A Non-parametric Bayesian Network Prior of Human Pose

Sebastian Nowozin, Peter V. Gehler, Andreas M. Lehrmann, 2013, 2013 IEEE International Conference on Computer Vision.

Perturb-and-MAP Random Fields: Reducing Random Sampling to Optimization, with Applications in Computer Vision

Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, 2014 .

Rehabilitating the Color Checker Dataset for Illuminant Estimation pdf

Peter V. Gehler, Simone Bianco, Mark S. Drew, 2018, CIC.

Rank : Non-Linear Object Detection

Luc Van Gool, Peter V. Gehler, Alain D. Lehmann, 2011 .

Kernel methods for remote sensing data analysis

Peter Gehler, Bernhard Scholkopf, 2009 .

Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers

Sebastian Nowozin, Peter V. Gehler, Peter Gehler, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Products of Edge-perts

Peter V. Gehler, Max Welling, M. Welling, 2005, NIPS.

Advanced Structured Prediction

Christoph H. Lampert, S. Todorovic, Jeremy Jancsary, 2014 .

Efficient 2D and 3D Facade Segmentation Using Auto-Context

Renaud Marlet, V. Jampani, Raghudeep Gadde, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.