Christopher Zach

Professor, Elektroteknik

zach@chalmers.se +46317726564 Hitta till mig

Christopher Zach är forskningsprofessor i forskargruppen Digitala bildsystem och bildanalys.

Publikationer
Samverkan och projekt

Senaste publikationer

2020
SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes

Författare: Pulak Purkait; Christopher Zach; Ian Reid

Publiceringsår: 2020

Publicerat i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publikationstyp: Paper i proceeding
Lifted Regression/Reconstruction Networks

Författare: Rasmus Kjær Høier; Christopher Zach

Publiceringsår: 2020

Publicerat i: 31st British Machine Vison Conference 2020, BMVC 2020

Publikationstyp: Paper i proceeding
Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning

Författare: Christopher Zach; Huu Le

Publiceringsår: 2020

Publicerat i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publikationstyp: Paper i proceeding
A Graduated Filter Method for Large Scale Robust Estimation

Författare: Huu Le; Christopher Zach

Publiceringsår: 2020

Publicerat i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Publikationstyp: Paper i proceeding
Progressive Batching for Efficient Non-linear Least Squares

Författare: Huu Le; Christopher Zach; Edward Rosten; Oliver Woodford

Publiceringsår: 2020

Publicerat i: Lecture Notes in Computer Science

Publikationstyp: Paper i proceeding
Contrastive learning for lifted networks

Författare: Christopher Zach; Virginia Estellers

Publiceringsår: 2020

Publicerat i: 30th British Machine Vision Conference 2019, BMVC 2019

Publikationstyp: Paper i proceeding
2019
Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

Författare: Pulak Purkait; Christopher Zach; Ian Reid

Publiceringsår: 2019

Publicerat i: 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)

Publikationstyp: Paper i proceeding
Pareto meets huber: Efficiently avoiding poor minima in robust estimation

Författare: Christopher Zach; Guillaume Bourmaud

Publiceringsår: 2019

Publicerat i: Proceedings of the IEEE International Conference on Computer Vision

Publikationstyp: Paper i proceeding

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Sidansvarig Publicerad: må 26 nov 2018.

Christopher Zach

Senaste publikationer

2020

2019

Projekt