Akata / Sattler / Geiger | Pattern Recognition | Buch | 978-3-030-71277-8 | sack.de

Buch, Englisch, Band 12544, 490 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 762 g

Reihe: Lecture Notes in Computer Science

Akata / Sattler / Geiger

Pattern Recognition

42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28 - October 1, 2020, Proceedings
1. Auflage 2021
ISBN: 978-3-030-71277-8
Verlag: Springer International Publishing

42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28 - October 1, 2020, Proceedings

Buch, Englisch, Band 12544, 490 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 762 g

Reihe: Lecture Notes in Computer Science

ISBN: 978-3-030-71277-8
Verlag: Springer International Publishing


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.

Akata / Sattler / Geiger Pattern Recognition jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


Normalizing Flow, Semantics, Physics, Camera Calibration.- Characterizing The Role of A Single Coupling Layer in Affine Normalizing Flows.- Semantic Bottlenecks: Quantifying & 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.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.