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Pattern Recognition

44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Björn Andres

    1. TU Dresden, Dresden, Germany

  2. Florian Bernard

    1. University of Bonn, Bonn, Germany

  3. Daniel Cremers

    1. Technical University of Munich, Munich, Germany

  4. Simone Frintrop

    1. University of Hamburg, Hamburg, Germany

  5. Bastian Goldlücke

    1. University of Konstanz, Konstanz, Germany

  6. Ivo Ihrke

    1. University of Siegen, Siegen, Germany

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13485)

Included in the following conference series:

Conference proceedings info: DAGM GCPR 2022.

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About this book

This book constitutes the refereed proceedings of the 44th DAGM German Conference on Pattern Recognition, DAGM GCPR 2022, which was held during September 27 – 30, 2022.

The 37 papers presented in this volume were carefully reviewed and selected from 78 submissions. They were organized in topical sections as follows: ​machine learning methods; unsupervised, semi-supervised and transfer learning; interpretable machine learning; low-level vision and computational photography; motion, pose estimation and tracking; 3D vision and stereo; detection and recognition; language and vision; scene understanding; photogrammetry and remote sensing; pattern recognition in the life and natural sciences; systems and applications.

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Table of contents (36 papers)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Machine Learning Methods

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. InvGAN: Invertible GANs

      • Partha Ghosh, Dominik Zietlow, Michael J. Black, Larry S. Davis, Xiaochen Hu
      Pages 3-19

    3. Auto-Compressing Subset Pruning for Semantic Image Segmentation

      • Konstantin Ditschuneit, Johannes S. Otterbach
      Pages 20-35

    4. Improving Robustness and Calibration in Ensembles with Diversity Regularization

      • Hendrik Alexander Mehrtens, Camila Gonzalez, Anirban Mukhopadhyay
      Pages 36-50

  3. Unsupervised, Semi-supervised and Transfer Learning

    1. Front Matter

      Pages 51-51
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    2. FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU

      • Daniel Pototzky, Azhar Sultan, Lars Schmidt-Thieme
      Pages 53-67

    3. Reiterative Domain Aware Multi-target Adaptation

      • Sudipan Saha, Shan Zhao, Nasrullah Sheikh, Xiao Xiang Zhu
      Pages 68-84

    4. Augmentation Learning for Semi-Supervised Classification

      • Tim Frommknecht, Pedro Alves Zipf, Quanfu Fan, Nina Shvetsova, Hilde Kuehne
      Pages 85-98

  4. Interpretable Machine Learning

    1. Front Matter

      Pages 99-99
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    2. Multi-attribute Open Set Recognition

      • Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta, Mauricio Munoz, Volker Fischer
      Pages 101-115

    3. A Syntactic Pattern Recognition Based Approach to Online Anomaly Detection and Identification on Electric Motors

      • Kutalmış Coşkun, Zeynep Kumralbaş, Hazel Çavuş, Borahan Tümer
      Pages 116-132

    4. Sparse Visual Counterfactual Explanations in Image Space

      • Valentyn Boreiko, Maximilian Augustin, Francesco Croce, Philipp Berens, Matthias Hein
      Pages 133-148

  5. Low-Level Vision and Computational Photography

    1. Front Matter

      Pages 149-149
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    2. Blind Single Image Super-Resolution via Iterated Shared Prior Learning

      • Thomas Pinetz, Erich Kobler, Thomas Pock, Alexander Effland
      Pages 151-165

    3. A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation

      • Dominik Drees, Florian Eilers, Ang Bian, Xiaoyi Jiang
      Pages 166-181

    4. Optimizing Edge Detection for Image Segmentation with Multicut Penalties

      • Steffen Jung, Sebastian Ziegler, Amirhossein Kardoost, Margret Keuper
      Pages 182-197

    5. Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning

      • Eric L. Wisotzky, Charul Daudkane, Anna Hilsmann, Peter Eisert
      Pages 198-212

  6. Motion, Pose Estimation and Tracking

    1. Front Matter

      Pages 213-213
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    2. SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection

      • Leonhard Sommer, Philipp Schröppel, Thomas Brox
      Pages 215-229
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Editors and Affiliations

  • TU Dresden, Dresden, Germany

    Björn Andres

  • University of Bonn, Bonn, Germany

    Florian Bernard

  • Technical University of Munich, Munich, Germany

    Daniel Cremers

  • University of Hamburg, Hamburg, Germany

    Simone Frintrop

  • University of Konstanz, Konstanz, Germany

    Bastian Goldlücke

  • University of Siegen, Siegen, Germany

    Ivo Ihrke

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