Peter V. Gehler

发表

Multi-View and 3D Deformable Part Models

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

Pattern Recognition

Peter V. Gehler, Juergen Gall, Bastian Leibe, 2015, Lecture Notes in Computer Science.

On feature combination for multiclass object classification

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

Multi-View Priors for Learning Detectors from Sparse Viewpoint Data pdf

Peter V. Gehler, Bernt Schiele, Bojan Pepik, 2014, ICLR.

Human Pose Estimation with Fields of Parts

Peter V. Gehler, Martin Kiefel, 2014, ECCV.

Kernel learning approaches for image classification

Peter V. Gehler, Peter Gehler, 2009 .

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.

Rehabilitating the Color Checker Dataset for Illuminant Estimation pdf

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

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.

Occlusion Patterns for Object Class Detection

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

Herding for Structured Prediction

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

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.

AD3: A Fast Decoder for Structured Prediction

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

The rate adapting poisson model for information retrieval and object recognition

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

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

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

Towards causal generative scene models via competition of experts pdf

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

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.

Generalized Sequential Tree-Reweighted Message Passing

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

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).

Superpixel Convolutional Networks Using Bilateral Inceptions

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

Deterministic Annealing for Multiple-Instance Learning

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

Permutohedral Lattice CNNs pdf

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

Pottics - The Potts Topic Model for Semantic Image Segmentation

Sebastian Nowozin, Peter V. Gehler, Christoph Dann, 2012, DAGM/OAGM Symposium.

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).

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

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

Input Image Detection GT Stage-1 Stage-1 Result Image Features Stage-2 Stage-2 Result Context Features Stage-3 Stage-3 Result Context Features

Peter V. Gehler, Varun Jampani, Renaud Marlet, 2018 .

A Generative Model of People in Clothing

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

Branch&Rank for Efficient Object Detection

Luc Van Gool, Peter V. Gehler, Alain D. Lehmann, 2013, International Journal of Computer Vision.

Sparse Convolutional Networks using the Permutohedral Lattice pdf

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

Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice pdf

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

Structured Prediction for Object Boundary Detection in Images

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

An introduction to kernel learning algorithms

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

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

Peter V. Gehler, Varun Jampani, Renaud Marlet, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Expressive Models and Comprehensive Benchmark for 2D Human Pose Estimation

Peter V. Gehler, Bernt Schiele, Mykhaylo Andriluka, 2014 .

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.

Visibility Maps for Improving Seam Carving

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

Structured Prediction for Event Detection

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

Learning Output Kernels with Block Coordinate Descent

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

Advanced Structured Prediction

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

Infinite Kernel Learning

Sebastian Nowozin, Peter V. Gehler, 2008, NIPS 2008.

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).

Branch&Rank: Non-Linear Object Detection

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

Products of Edge-perts

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

Semantic Video CNNs Through Representation Warping

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

Video Propagation Networks

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

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

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

PAC-Bayesian Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction

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

Learning to Filter Object Detections

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

Scene Carving: Scene Consistent Image Retargeting

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

The Informed Sampler: A Discriminative Approach to Bayesian Inference in Computer Vision

Sebastian Nowozin, Peter V. Gehler, Varun Jampani, 2014 .

Teaching 3D geometry to deformable part models

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

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..

Barrista: Caffe Well-Served

Peter V. Gehler, Daniel Kappler, Christoph Lassner, 2016, ACM Multimedia.

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.

Intrinsic Video

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

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).

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).

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).

Efficient Facade Segmentation Using Auto-context

Peter V. Gehler, Varun Jampani, Raghudeep Gadde, 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

Dynamic Structured Model Selection

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

Poselet Conditioned Pictorial Structures

Peter V. Gehler, Bernt Schiele, Mykhaylo Andriluka, 2013, 2013 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.

Training Structured Predictors Through Iterated Logistic Regression

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

Structured Learning from Cheap Data

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

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

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

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).

3D2PM - 3D Deformable Part Models

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

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain pdf

Peter V. Gehler, Bernhard Schölkopf, Matthias Bethge, 2021, ArXiv.

Towards Total Recall in Industrial Anomaly Detection pdf

Peter V. Gehler, Bernhard Schölkopf, Thomas Brox, 2021, ArXiv.

Backward-Compatible Prediction Updates: A Probabilistic Approach pdf

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

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

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

Optimizing the Measure of Performance

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

Rank : Non-Linear Object Detection

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