000 | 05750nam a22006375i 4500 | ||
---|---|---|---|
001 | 978-3-030-71278-5 | ||
003 | DE-He213 | ||
005 | 20240730171146.0 | ||
007 | cr nn 008mamaa | ||
008 | 210316s2021 sz | s |||| 0|eng d | ||
020 |
_a9783030712785 _9978-3-030-71278-5 |
||
024 | 7 |
_a10.1007/978-3-030-71278-5 _2doi |
|
050 | 4 | _aQ337.5 | |
050 | 4 | _aTK7882.P3 | |
072 | 7 |
_aUYQP _2bicssc |
|
072 | 7 |
_aCOM016000 _2bisacsh |
|
072 | 7 |
_aUYQP _2thema |
|
082 | 0 | 4 |
_a006.4 _223 |
245 | 1 | 0 |
_aPattern Recognition _h[electronic resource] : _b42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28 - October 1, 2020, Proceedings / _cedited by Zeynep Akata, Andreas Geiger, Torsten Sattler. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
|
300 |
_aXV, 490 p. 13 illus. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12544 |
|
505 | 0 | _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. | |
520 | _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. | ||
650 | 0 |
_aPattern recognition systems. _93953 |
|
650 | 0 |
_aMachine learning. _91831 |
|
650 | 0 |
_aData structures (Computer science). _98188 |
|
650 | 0 |
_aInformation theory. _914256 |
|
650 | 0 |
_aApplication software. _997332 |
|
650 | 0 |
_aComputer science _xMathematics. _93866 |
|
650 | 1 | 4 |
_aAutomated Pattern Recognition. _931568 |
650 | 2 | 4 |
_aMachine Learning. _91831 |
650 | 2 | 4 |
_aData Structures and Information Theory. _931923 |
650 | 2 | 4 |
_aComputer and Information Systems Applications. _997335 |
650 | 2 | 4 |
_aMathematics of Computing. _931875 |
700 | 1 |
_aAkata, Zeynep. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _997338 |
|
700 | 1 |
_aGeiger, Andreas. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _997340 |
|
700 | 1 |
_aSattler, Torsten. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _997341 |
|
710 | 2 |
_aSpringerLink (Online service) _997344 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030712778 |
776 | 0 | 8 |
_iPrinted edition: _z9783030712792 |
830 | 0 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12544 _997345 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-71278-5 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
912 | _aZDB-2-LNC | ||
942 | _cELN | ||
999 |
_c87424 _d87424 |