Dr. Christian Eggert

Ehemaliger wissenschaftlicher Mitarbeiter
Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Telefon: N/A


  • (Small) Object detection

  • Compact image signatures

  • Large-scale image retrieval

  • Locality-sensitive hashing


Christian Eggert. Reliable Company Logo Detection with Deep Convolutional Neural Networks Addressing the Small Object Detection Problem.
Dissertation, University of Augsburg, Feb. 8, 2019. ( Official) [ PDF]



  • Dan Zecha, Christian Eggert, Moritz Einfalt, Stephan Brehm, Rainer Lienhart.
    A Convolutional Sequence to Sequence Model for Multimodal Dynamics Prediction in Ski Jumps.
    First International ACM Workshop on Multimodal Content Analysis in Sports (ACM MMSports'18), part of ACM Multimedia 2018. Seoul, Korea, October 2018. [ PDF]
  • Dan Zecha, Moritz Einfalt, Christian Eggert, Rainer Lienhart.
    Kinematic Pose Rectification for Performance Analysis and Retrieval in Sports.
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2018. Salt Lake City, USA, June 2018. [ PDF]
  • Philipp Harzig, Christian Eggert, Rainer Lienhart. 
    Visual Question Answering With a Hybrid Convolution Recurrent Model.
    ACM International Conference on Multimedia Retrieval 2018 (ACM ICMR 2018), Yokohama, June 2018 [ PDF]
  • Christian Eggert, Stephan Brehm, Anton Winschel, Dan Zecha, Rainer Lienhart. 
    A Closer Look: Small Object Detection in Faster R-CNN.
    IEEE International Conference on Multimedia and Expo 2017 (ICME 2017). Hong Kong, China, July 2017. [ PDF ]

  • Christian Eggert, Stephan Brehm, Dan Zecha, Rainer Lienhart.
    Improving Small Object Proposals for Company Logo Detection.
    ACM International Conference on Multimedia Retrieval 2017 (ICMR 2017). Bucharest, Romania, June 2017. [ arXiv] [ PDF]

  • Dan Zecha, Christian Eggert, Rainer Lienhart.
    Pose Estimation for Deriving Kinematic Parameters of Competitive Swimmers.
    Computer Vision Applications in Sports, part of IS&T Electronic Imaging 2017. Burlingame, California, January 2017. [ PDF ]

  • Christian Eggert, Anton Winschel, Dan Zecha, Rainer Lienhart.
    Saliency-guided Selective Magnification for Company Logo Detection.
    International Conference on Pattern Recognition 2016 (ICPR 2016), Cancun, December 2016. [ PDF ]

  • Anton Winschel, Rainer Lienhart, Christian Eggert.
    Diversity in Object Proposal.
    arXiv:1603.04308 [cs.CV], March 2016 [ PDF]

  • Christian Eggert, Anton Winschel, Rainer Lienhart.
    On the Benefit of Synthetic Data for Company Logo Detection.
    ACM Multimedia 2015 (ACMMM 2015), Brisbane, October 2015. [ PDF ]

  • Fabian Richter, Christian Eggert, Rainer Lienhart.
    Fisher Vector Encoding of Micro Color Features for (Real World) Jigsaw Puzzles
    International Conference on Document Analysis and Recognition (ICDAR) 2015, Nancy, August 2015 [ IEEE Xplore]

  • Christian Eggert, Stefan Romberg, Rainer Lienhart
    Improving VLAD: Hierarchical Coding and a refined Local Coordinate System.
    IEEE International Conference on Image Processing 2014, Paris, October 2014. [ PDF ]


  • Christian Eggert. Implementation and Evaluation of the gPb Contour Detector. August 2012. [ PDF ]

Lehre in vergangenen Semestern

  • WS 2018: Grundlagen der Signalverarbeitung und des maschinellen Lernens
  • WS 2018: Seminar: Multimediale Datenverarbeitung
  • SS 2018: Multimedia Projekt
  • SS 2018: Seimnar: Multimedia und Maschinelles Sehen
  • WS 2017: Multimedia Grundlagen I (wechselnde Dozenten)
  • WS 2017: Seminar: Multimediale Datenverarbeitung
  • SS 2017: Bayesian Networks
  • SS 2017: Seimnar: Multimedia und Maschinelles Sehen
  • WS 2016: Seminar: Multimediale Datenverarbeitung
  • SS 2016: Bayesian Networks
  • SS 2016: Seimnar: Multimedia und Maschinelles Sehen
  • WS 2015: Multimedia Projekt
  • WS 2015: Seminar: Multimediale Datenverarbeitung
  • SS 2015: Bayesian Networks
  • SS 2015: Seimnar: Multimedia und Maschinelles Sehen
  • WS 2014: Seminar: Multimediale Datenverarbeitung
  • SS 2014: Seimnar: Multimedia und Maschinelles Sehen
  • WS 2013: Seminar: Multimediale Datenverarbeitung
  • SS 2013: Multimedia II: Machien Learning & Computer Vision