LEADER 05742nam 22007575 450 001 9910483991503321 005 20250626164203.0 010 $a3-030-71278-8 024 7 $a10.1007/978-3-030-71278-5 035 $a(CKB)4100000011801755 035 $a(MiAaPQ)EBC6521520 035 $a(Au-PeEL)EBL6521520 035 $a(OCoLC)1243544680 035 $a(PPN)254719155 035 $a(MiAaPQ)EBC29095484 035 $a(DE-He213)978-3-030-71278-5 035 $a(EXLCZ)994100000011801755 100 $a20210316d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition $e42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28 ? October 1, 2020, Proceedings /$fedited by Zeynep Akata, Andreas Geiger, Torsten Sattler 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (504 pages) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12544 311 08$a3-030-71277-X 327 $aNormalizing 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. 330 $aThis book constitutes the refereed proceedings of the 42nd German Conference on Pattern Recognition, DAGM GCPR 2020, which took place during September 28 until October 1, 2020. The conference was planned to take place in Tübingen, Germany, but had to change to an online format due to the COVID-19 pandemic. The 34 papers presented in this volume were carefully reviewed and selected from a total of 89 submissions. They were organized in topical sections named: Normalizing Flow, Semantics, Physics, Camera Calibration and Computer Vision, Pattern Recognition, Machine Learning. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12544 606 $aPattern recognition systems 606 $aMachine learning 606 $aData structures (Computer science) 606 $aInformation theory 606 $aApplication software 606 $aComputer science$xMathematics 606 $aAutomated Pattern Recognition 606 $aMachine Learning 606 $aData Structures and Information Theory 606 $aComputer and Information Systems Applications 606 $aMathematics of Computing 615 0$aPattern recognition systems. 615 0$aMachine learning. 615 0$aData structures (Computer science) 615 0$aInformation theory. 615 0$aApplication software. 615 0$aComputer science$xMathematics. 615 14$aAutomated Pattern Recognition. 615 24$aMachine Learning. 615 24$aData Structures and Information Theory. 615 24$aComputer and Information Systems Applications. 615 24$aMathematics of Computing. 676 $a006.4 676 $a006.4 702 $aAkata$b Zeynep 702 $aGeiger$b Andreas$f1947- 702 $aSattler$b Torsten 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483991503321 996 $aPattern Recognition$9381471 997 $aUNINA