1 option
Pattern Recognition : 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28 - October 1, 2020, Proceedings / edited by Zeynep Akata, Andreas Geiger, Torsten Sattler.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12544
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12544
- Language:
- English
- Subjects (All):
- Pattern recognition systems.
- Machine learning.
- Data structures (Computer science).
- Information theory.
- Application software.
- Computer science-Mathematics.
- Automated Pattern Recognition.
- Machine Learning.
- Data Structures and Information Theory.
- Computer and Information Systems Applications.
- Mathematics of Computing.
- Local Subjects:
- Automated Pattern Recognition.
- Machine Learning.
- Data Structures and Information Theory.
- Computer and Information Systems Applications.
- Mathematics of Computing.
- Physical Description:
- 1 online resource (XV, 490 pages) : 13 illustrations
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2021.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of the 42nd German Conference on Pattern Recognition, DAGM GCPR 2020, which took place during September 28 until October 1, 2020. The conference was planned to take place in Tübingen, Germany, but had to change to an online format due to the COVID-19 pandemic. The 34 papers presented in this volume were carefully reviewed and selected from a total of 89 submissions. They were organized in topical sections named: Normalizing Flow, Semantics, Physics, Camera Calibration and Computer Vision, Pattern Recognition, Machine Learning.
- Contents:
- Normalizing Flow, Semantics, Physics, Camera Calibration
- Characterizing The Role of A Single Coupling Layer in Affine Normalizing Flows
- Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations
- Bias Detection and Prediction of Mapping Errors in Camera Calibration
- Learning to Identify Physical Parameters from Video Using Differentiable Physics
- Computer Vision, Pattern Recognition, Machine Learning
- Assignment Flow For Order-Constrained OCT Segmentation
- Boosting Generalization in Bio-Signal Classification by Learning the Phase-Amplitude Coupling
- Long-Tailed Recognition Using Class-Balanced Experts
- Analyzing the Dependency of ConvNets on Spatial Information
- Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels
- Observer Dependent Lossy Image Compression
- Adversarial Synthesis of Human Pose from Text
- Long-Term Anticipation of Activities with Cycle Consistency
- Multi-Stage Fusion for One-click Segmentation
- Neural Architecture Performance Prediction Using Graph Neural Networks
- Discovering Latent Classes for Semi-Supervised Semantic Segmentation
- Riemannian SOS-Polynomial Normalizing Flows
- Automated water segmentation and river level detection on camera images using transfer learning
- Does SGD Implicitly Optimize for Smoothness
- Looking outside the box: The role of context in Random Forest based semantic segmentation of PolSAR images
- Haar Wavelet based Block Autoregressive Flows for Trajectories
- Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding
- Constellation Codebooks for Reliable Vehicle Localization
- Towards Bounding-Box Free Panoptic Segmentation
- Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks
- Unsupervised Part Discovery by Unsupervised Disentanglement
- On the Lifted Multicut Polytope for Trees
- Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
- Image Inpainting with Learnable Feature Imputation
- 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
- Inline Double Layer Depth Estimation with Transparent Materials
- A Differentiable Convolutional Distance Transform Layer for Improved Image Segmentation
- PET-guided Attention Network for Segmentation of Lung Tumors from PET/CT images
- Self-supervised Disentanglement of Modality-specific and Shared Factors Improves Multimodal Generative Models
- Multimodal semantic forecasting based on conditional generation of future features.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-71278-5
- 9783030712785
- Access Restriction:
- Restricted for use by site license.
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