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Dr. Philip Häusser

AlumniTechnical University of Munich

Department of Informatics
Informatics 9
Boltzmannstrasse 3
85748 Garching
Germany

Fax: +49-89-289-17757
Office: 
Mail: haeusser@cs.tum.edu

About

Please find my dissertation (Doktorarbeit) here: https://mediatum.ub.tum.de/1444880

I finished my PhD in computer science at TUM 2018. My focus was research in computer vision and machine learning (colloquially known as "artificial intelligence") advised by Prof. Daniel Cremers.

I hold a Master's degree in physics from the University of California, Santa Cruz (USA) and a Bachelor's degree in physics from the LMU Munich where I was working at the cavity quantum optics group headed by Prof. T.W. Haensch.

When I'm not in the lab you might find me playing squash or volleyball or you might encounter one of my TV productions.

I will be on leave from May 2017 – September 2017 for an internship at Google.

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More about my research at Google can be found here and here.

Deep learning projects

  • Optical flow estimation (coop with Freiburg; ICCV paper 2015)
  • Scene flow estimation (coop with Freiburg; CVPR paper 2016)
  • Facial expression recognition (student project)
  • Mathematical handwriting recognition (Bachelor thesis)
  • Video frame prediction (Master's thesis)
  • Construction zone recognition for self-driving cars (Master's thesis)
  • Semi-supervised training "learning by association" (Google internship 2016; CVPR paper 2017)
  • Domain adaptation with neural networks

Teaching

Summer Term 2015

Summer Term 2016

Winter Term 2016/17

Summer Term 2018

Publications


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Conference and Workshop Papers
2018
[]Associative Deep Clustering - Training a Classification Network with no Labels (P. Haeusser, J. Plapp, V. Golkov, E. Aljalbout and D. Cremers), In Proc. of the German Conference on Pattern Recognition (GCPR), 2018.  [bibtex] [pdf]
2017
[]Associative Domain Adaptation (P. Haeusser, T. Frerix, A. Mordvintsev and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]Better Text Understanding Through Image-To-Text Transfer (K. Kurach, S. Gelly, M. Jastrzebski, P. Haeusser, O. Teytaud, D. Vincent and O. Bousquet), In arxiv:1705.08386, 2017.  [bibtex] [pdf]
[]Learning by Association - A versatile semi-supervised training method for neural networks (P. Haeusser, A. Mordvintsev and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
2015
[]FlowNet: Learning Optical Flow with Convolutional Networks (A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bibtex] [doi] [pdf]
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Talks

  • Challenges in Dynamic Imaging Data, June 9th 2015, Cambridge University slides
  • Deep Learning and Convolutional Networks, July 9th 2015, University of Augsburg
  • Kuenstliche Intelligenz und Digitalisierung, April 8th, 2017, Akademie fuer politische Bildung, Tutzing

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

05.03.2021

Maks Ovsjanikov (Ecole Polytechnique) will give a talk in the TUM AI lecture series on March 11th, 3pm! Livestream

02.03.2021

We have seven papers (4 orals, 3 posters) accepted to CVPR 2021!

02.03.2021

We have two papers accepted to ICRA 2021!

01.03.2021

Marc Pollefeys (ETH Zurich) will give a talk in the TUM AI lecture series on March 4th, 3pm! Livestream

25.01.2021

Rick Szeliski (University of Washington) will give a talk in the TUM AI lecture series on Jan 28th, 5pm! Livestream

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