Christopher Zach

Forskningsprofessor at Imaging and Image Analysis

Christopher Zach is a research professor in the research group Computer vision and medical image analysis. His main research interests are 3D reconstruction from images and 3D image understanding. Further, he conducts research in efficient and real-time methods for computer vision and machine learning, and therefore he also explores underlying mathematical models and suitable numerical optimization methods.

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Showing 8 publications

2020

SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes

Pulak Purkait, Christopher Zach, Ian Reid
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 12369 LNCS, p. 155-171
Paper in proceedings
2020

Progressive Batching for Efficient Non-linear Least Squares

Huu Le, Christopher Zach, Edward Rosten et al
Lecture Notes in Computer Science
Paper in proceedings
2020

Contrastive learning for lifted networks

Christopher Zach, Virginia Estellers
30th British Machine Vision Conference 2019, BMVC 2019
Paper in proceedings
2020

Lifted Regression/Reconstruction Networks

Rasmus Kjær Høier, Christopher Zach
31st British Machine Vison Conference 2020, BMVC 2020
Paper in proceedings
2020

A Graduated Filter Method for Large Scale Robust Estimation

Huu Le, Christopher Zach
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 5558-5567
Paper in proceedings
2020

Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning

Christopher Zach, Huu Le
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 12371 LNCS, p. 464-480
Paper in proceedings
2019

Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

Pulak Purkait, Christopher Zach, Ian Reid
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), p. 1998-2005
Paper in proceedings
2019

Pareto meets huber: Efficiently avoiding poor minima in robust estimation

Christopher Zach, Guillaume Bourmaud
Proceedings of the IEEE International Conference on Computer Vision. Vol. 2019-October, p. 10242-10250
Paper in proceedings

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Showing 3 research projects

2020–

Learning and Leveraging Rich Priors for Factorization Problems

Christopher Zach Imaging and Image Analysis
Carl Olsson Imaging and Image Analysis
Wallenberg AI, Autonomous Systems and Software Program

2020–2025

Energy-based models for supervised deep neural networks and their applications

Christopher Zach Imaging and Image Analysis
Morteza Haghir Chehreghani Data Science
Chalmers AI Research Centre (CHAIR)
Chalmers AI Research Centre

2020–2022

AI for Analysis for Naturalistic Driving Data

Christopher Zach Imaging and Image Analysis
Marco Dozza Crash Analysis and Prevention
Dag Wedelin Data Science
Chalmers AI Research Centre
AoA Transport Funds

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