Björn Stenger

Group Leader, Lead Scientist
Rakuten Institute of Technology (RIT)

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About me

I am a group leader and researcher at the Rakuten Institute of Technology. My current interests are in machine learning applied to computer vision, video processing and generative design.

Prior to Rakuten I was at Toshiba Research (archived page). I completed my PhD in computer vision at the University of Cambridge in Roberto Cipolla's group, co-supervised by Philip Torr. My Diplom in Computer Science is from the University of Bonn. During my studies, I spent time at the University of Victoria, Hewlett-Packard, and Siemens Corporate Research.

News

2019/05 - Welcome to Panayiotis Panayiotou (Pani) (ETH Zurich) as summer intern!

2019/04 - Welcome to Lu Yan (Tokyo Tech)!

2019/03 - Two papers accepted at MVA 2019

2019/02 - Welcome Yuan Chen (University of Waterloo) as intern!

2018/11 - ICDM paper: Deep Heterogeneous Autoencoders for Collaborative Filtering

2018/10 - ICIP paper: Dense ByNet: Residual Dense Network for Image Super Resolution

2018/10 - Computer vision internships 2019 available for graduate students. Thanks to everyone who applied!

2018/09 - Welcome Ryo Sobue, Ray Sekine Choi, and Chenfei Wang as an interns!

2018/08 - TIP paper: Trajectories As Topics: Multi-Object Tracking by Topic Discovery (extension of AAAI 2015 paper)

Recent work

Trajectories as Topics: Multi-Object Tracking by Topic Discovery
W. Luo, B. Stenger, X. Zhao, T.-K.Kim
IEEE Trans. Image Processing, Vol. 29(1), January 2019.

Multi-target object tracking with a topic discovery approach.

[bibtex]
@InJournal{LuoTIP2019},
  title = {Trajectories as Topics: Multi-Object Tracking by Topic Discovery},
  author = {Luo, W. and Stenger, B. and Zhao, X. and Kim, T.-K.},
  booktitle = {IEEE Trans. Image Processing},
  year = {2019},
  volume = {29},
  number = {1},
  month = {January},
  pdf = {luo_tip2018.pdf}
  }
    
Deep Heterogeneous Autoencoders for Collaborative Filtering
T. Li, Y. Ma, J. Xu, B. Stenger, C. Liu, Y. Hirate.
IEEE International Conference on Data Mining (ICDM), November 2018.

Autoencoder-based recommender system that takes into account multiple data sources, including the purchase history.

[bibtex]
@InProceedings{LiICDM2018,
  author = {Li, T. and Ma, Y. and Xu, J. and Stenger, B. and Liu, C. 
            and Hirate, Y.},
  title  = {Deep Heterogeneous Autoencoders for Collaborative Filtering},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year   = {2018},
  month = {November},
  pdf = {li_icdm2018.pdf}
}
    
RGB-based 3D Hand Pose Estimation via Privileged Learning with Depth Images
S. Yuan, B. Stenger, T.-K. Kim,
arxiv, Nov 2018.

A method for accurate RGB hand pose estimation, making use of a large training corpus of annotated depth data.

[arxiv] [bibtex]
@InProceedings{YuanARXIV2018,
  author =   {Yuan, S. and Stenger, B. and Kim, T.-K.},
  title =    {RGB-based 3D Hand Pose Estimation via Privileged Learning 
              with Depth Images},
  booktitle = {arxiv},
  year =      {2018},
  month =     {November},
  pdf = {yuan_arxiv2018.pdf},
  arxiv = {https://arxiv.org/abs/1811.07376}
}
    
Dense ByNet: Residual Dense Network for Image Super Resolution
J. Xu, Y. Chae, B. Stenger, A. Datta
ICIP, October 2018.

CNN-based image super-resolution with high speed & low distortion, improved over ICIP 2017 paper.

[bibtex]
@InProceedings{XuICIP2018,
  author = {Xu, J. and Chae, Y. and Stenger, B. and Datta, A.},
  title  = {Dense ByNet: Residual Dense Network for Image Super Resolution},
  booktitle = {Int. Conf. Image Processing (ICIP)},
  year =      {2018},
  month =     {October},
  pdf = {xu_icip2018.pdf}
}
    
3D Hand Pose Estimation: From Current Achievements to Future Goals
S. Yuan, G. Garcia-Hernando, B. Stenger, G. Moon, J. Y. Chang, K. M. Lee, P. Molchanov, J. Kautz, S. Honari, L. Ge, J. Yuan, X. Chen, G. Wang, F. Yang, K. Akiyama, Y. Wu, Q. Wan, M. Madadi, S. Escalera, S. Li, D. Lee, I. Oikonomidis, A. Argyros, and T.-K. Kim,
CVPR, June 2018.

Analysis of the top performing methods in the Hands in the Million Challenge.

[arxiv] [bibtex]
@InProceedings{YuanCVPR2018,
  author =   {Yuan, S. and Garcia-Hernando, G. and Stenger, B. and  Moon, G. 
              and  Chang, J. Y. and Lee, K. M. and  Molchanov, P. and 
              Kautz, J. and Honari, S. and  Ge, L. and  Yuan, J. and  
              Chen, X. and Wang, G. and Yang, F. and Akiyama, K. and Wu, Y. 
              and Wan, Q. and Madadi, M. and Escalera, S. and Li, S. and 
              Lee, D. and Oikonomidis, I. and Argyros, A. and Kim, T.-K.},
  title =    {3D Hand Pose Estimation: From Current Achievements 
              to Future Goals},
  booktitle = {CVPR},
  year =      {2018},
  month =     {June}
}
    
ByNet-SR: Image Super Resolution with a Bypass Connection Network
J. Xu, Y. Chae, B. Stenger,
ICIP, September 2017.

CNN-based image super-resolution with high speed & quality.

[bibtex][code]
@InProceedings{XuICIP2017,
  author = {Xu, J. and Chae, Y. and Stenger, B.},
  title  = {{ByNet-SR}: Image Super Resolution with a 
            Bypass Connection Network},
  year   = {2017},
  month = {September},
  booktitle = {ICIP}
}
    
BigHand2.2M Benchmark: Hand Pose Data Set and State of the Art Analysis
S. Yuan, Q. Ye, B. Stenger, S. Jain, T.-K. Kim,
CVPR, July 2017.

We captured a new standard dataset for 3D hand pose estimation. Depth maps are accurately annotated with 3D joint locations using a magnetic tracking system. We show that training a CNN on this data achieves accurate results. The data was used in the Hands in the Million Challenge.

[arxiv] [bibtex]

@InProceedings{YuanCVPR2017,
  author =   {Yuan, S. and Ye, Q. and Stenger, B. 
              and Jain, S. and Kim, T.-K."},
  title =    {BigHand2.2M Benchmark: Hand Pose Data Set 
              and State of the Art Analysis},
  booktitle = {CVPR},
  year =      {2017},
  month =     {July}
}
    
Parsing Floor Plan Images
S. Dodge, J. Xu, B. Stenger,
MVA, May 2017.

Wall segmentation using fully convolutional networks (FCN) and applications in furniture fitting and 3D modeling

[R-FP dataset] [video] [bibtex]
@InProceedings{DodgeMVA2017,
   author  = {Dodge, S. and Xu, J. and Stenger, B.},
   title   = {Parsing Floor Plan Images},
   year    = {2017},
   month   = {May},
   booktitle = {MVA}
}
    
Pano2CAD: Room Layout From A Single Panorama Image
J. Xu, B. Stenger, T. Kerola, T. Tung,
WACV, March 2017.

Estimating 3D room geometry from a single panorama image using surface normal estimation, 2D object detection and 3D object pose estimation

[arxiv] [bibtex]
@InProceedings{XuWACV2017,
   author  = {Xu, J. and Stenger, B. and Kerola T. and Tung, T.},
   title   = {Pano2CAD: Room Layout From A Single Panorama Image},
   year    = {2017},
   month   = {March},
   booktitle = {WACV}
}
    
design credits