Pattern Recognition

43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 - October 1, 2021, Proceedings
1. Auflage 2021
ISBN: 978-3-030-92658-8
Verlag: Springer International Publishing

Buch, Englisch, 726 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1107 g

Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics

This book constitutes the refereed proceedings of the 43 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.

Zielgruppe

Research

Autoren/Hrsg.

Bauckhage, Christian

Schwing, Alexander

Gall, Juergen

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Machine Learning and Optimization.- Sublabel-Accurate Multilabeling Meets Product Label Spaces.- InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization.- Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise.- Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data.- Revisiting Consistency Regularization for Semi-Supervised Learning.- Learning Robust Models Using the Principle of Independent Causal Mechanisms.- Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks.- Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators.- End-to-end Learning of Fisher Vector Encodings for Part Features in Fine-grained Recognition.- Investigating the Consistency of Uncertainty Sampling in Deep Active Learning.- ScaleNet: An Unsupervised Representation Learning Method for Limited Information.- Actions, Events, and Segmentation.- A New Split for Evaluating True Zero-Shot Action Recognition.- Video Instance Segmentation with Recurrent Graph Neural Networks.- Distractor-Aware Video Object Segmentation.- (SP)^2Net for Generalized Zero-Label Semantic Segmentation.- Contrastive Representation Learning for Hand Shape Estimation.- Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks.- FIFA: Fast Inference Approximation for Action Segmentation.- Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision.- A Comparative Study of PnP and Learning Approaches to Super-Resolution in a Real-World Setting.- Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing.- Spatiotemporal Outdoor Lighting Aggregation on Image Sequences.- Generative Models and Multimodal Data.- AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style.- Learning Conditional Invariance through Cycle Consistency.- CAGAN: Text-To-Image Generation with Combined Attention Generative Adversarial Networks.- TxT: Crossmodal End-to-End Learning with Transformers.- Diverse Image Captioning with Grounded Style.- Labeling and Self-Supervised Learning.- Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-Labeling.- Quantifying Uncertainty of Image Labelings Using Assignment Flows.- Implicit and Explicit Attention for Zero-Shot Learning.- Self-Supervised Learning for Object Detection in Autonomous Driving.- Assignment Flows and Nonlocal PDEs on Graphs.- Applications.- Viewpoint-Tolerant Semantic Segmentation for Aerial Logistics.- T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression.- TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases.- Detecting Slag Formations with Deep Convolutional Neural Networks.- Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture.- Weakly Supervised Segmentation Pre-training for Plant Cover Prediction.- How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?.- 3D Modeling and Reconstruction.- Clustering Persistent Scatterer Points Based on a Hybrid Distance Metric.- CATEGORISE: An Automated Framework for Utilizing the Workforce of the Crowd for Semantic Segmentation of 3D Point Clouds.- Zero-Shot remote sensing image super resolution based on image continuity and self-tessellations.- A Comparative Survey of Geometric Light Source Calibration Methods.- Quantifying point cloud realism through adversarially learned latent representations.- Full-Glow: Fully conditional Glow for more realistic image generation.- Multidirectional Conjugate Gradients for Scalable Bundle Adjustment.