Pattern Recognition : 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings / edited by Christian Bauckhage, Juergen Gall, Alexander Schwing.

Format:

Book

Contributor:

Bauckhage, Christian, editor.

Gall, Juergen, editor.

Schwing, Alexander, editor.

Series:

Image Processing, Computer Vision, Pattern Recognition, and Graphics, 3004-9954 ; 13024

Language:

English

Subjects (All):

Pattern recognition systems.

Machine learning.

Computer vision.

Computer engineering.

Computer networks.

Social sciences--Data processing.

Social sciences.

Education--Data processing.

Education.

Automated Pattern Recognition.

Machine Learning.

Computer Vision.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Computers and Education.

Local Subjects:

Automated Pattern Recognition.

Machine Learning.

Computer Vision.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Computers and Education.

Physical Description:

1 online resource (734 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

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.

Contents:

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. .

Other Format:

Print version: Bauckhage, Christian Pattern Recognition

ISBN:

3-030-92659-1