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Pattern Recognition

43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Christian Bauckhage

    1. Fraunhofer IAIS, Sankt Augustin, Germany

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  2. Juergen Gall

    1. University of Bonn, Bonn, Germany

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    You can also search for this editor in PubMed   Google Scholar

  3. Alexander Schwing

    1. University of Illinois at Urbana-Champaign, Urbana, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13024)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: DAGM GCPR 2021.

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Table of contents (46 papers)

  1. Front Matter

    Pages i-xvii
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  2. Machine Learning and Optimization

    1. Front Matter

      Pages 1-1
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    2. Sublabel-Accurate Multilabeling Meets Product Label Spaces

      • Zhenzhang Ye, Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers
      Pages 3-17

    3. InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization

      • Robert Harb, Patrick Knöbelreiter
      Pages 18-32

    4. Sampling-Free Variational Inference for Neural Networks with Multiplicative Activation Noise

      • Jannik Schmitt, Stefan Roth
      Pages 33-47

    5. Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data

      • Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler
      Pages 48-62

    6. Revisiting Consistency Regularization for Semi-supervised Learning

      • Yue Fan, Anna Kukleva, Bernt Schiele
      Pages 63-78

    7. Learning Robust Models Using the Principle of Independent Causal Mechanisms

      • Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Köthe
      Pages 79-110

    8. Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks

      • Alexander Shekhovtsov, Viktor Yanush
      Pages 111-126

    9. Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators

      • Alexander Shekhovtsov
      Pages 127-141

    10. End-to-End Learning of Fisher Vector Encodings for Part Features in Fine-Grained Recognition

      • Dimitri Korsch, Paul Bodesheim, Joachim Denzler
      Pages 142-158

    11. Investigating the Consistency of Uncertainty Sampling in Deep Active Learning

      • Niklas Penzel, Christian Reimers, Clemens-Alexander Brust, Joachim Denzler
      Pages 159-173

    12. ScaleNet: An Unsupervised Representation Learning Method for Limited Information

      • Huili Huang, M. Mahdi Roozbahani
      Pages 174-188

  3. Actions, Events, and Segmentation

    1. Front Matter

      Pages 189-189
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    2. A New Split for Evaluating True Zero-Shot Action Recognition

      • Shreyank N. Gowda, Laura Sevilla-Lara, Kiyoon Kim, Frank Keller, Marcus Rohrbach
      Pages 191-205

    3. Video Instance Segmentation with Recurrent Graph Neural Networks

      • Joakim Johnander, Emil Brissman, Martin Danelljan, Michael Felsberg
      Pages 206-221

    4. Distractor-Aware Video Object Segmentation

      • Andreas Robinson, Abdelrahman Eldesokey, Michael Felsberg
      Pages 222-234

    5. (SP)\(^2\)Net for Generalized Zero-Label Semantic Segmentation

      • Anurag Das, Yongqin Xian, Yang He, Bernt Schiele, Zeynep Akata
      Pages 235-249

    6. Contrastive Representation Learning for Hand Shape Estimation

      • Christian Zimmermann, Max Argus, Thomas Brox
      Pages 250-264

    7. Fusion-GCN: Multimodal Action Recognition Using Graph Convolutional Networks

      • Michael Duhme, Raphael Memmesheimer, Dietrich Paulus
      Pages 265-281
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Keywords

About this book

This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic.

The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.

Editors and Affiliations

  • Fraunhofer IAIS, Sankt Augustin, Germany

    Christian Bauckhage

  • University of Bonn, Bonn, Germany

    Juergen Gall

  • University of Illinois at Urbana-Champaign, Urbana, USA

    Alexander Schwing

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