000 | 06977nam a22006615i 4500 | ||
---|---|---|---|
001 | 978-3-030-92659-5 | ||
003 | DE-He213 | ||
005 | 20240423125542.0 | ||
007 | cr nn 008mamaa | ||
008 | 220113s2021 sz | s |||| 0|eng d | ||
020 |
_a9783030926595 _9978-3-030-92659-5 |
||
024 | 7 |
_a10.1007/978-3-030-92659-5 _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] : _b43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings / _cedited by Christian Bauckhage, Juergen Gall, Alexander Schwing. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
|
300 |
_aXVII, 726 p. 98 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 ; _v13024 |
|
505 | 0 | _aMachine 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. . | |
520 | _aThis 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. | ||
650 | 0 | _aPattern recognition systems. | |
650 | 0 | _aMachine learning. | |
650 | 0 | _aComputer vision. | |
650 | 0 | _aComputer engineering. | |
650 | 0 | _aComputer networks . | |
650 | 0 |
_aSocial sciences _xData processing. |
|
650 | 0 |
_aEducation _xData processing. |
|
650 | 1 | 4 | _aAutomated Pattern Recognition. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aComputer Vision. |
650 | 2 | 4 | _aComputer Engineering and Networks. |
650 | 2 | 4 | _aComputer Application in Social and Behavioral Sciences. |
650 | 2 | 4 | _aComputers and Education. |
700 | 1 |
_aBauckhage, Christian. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aGall, Juergen. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aSchwing, Alexander. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030926588 |
776 | 0 | 8 |
_iPrinted edition: _z9783030926601 |
830 | 0 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v13024 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-92659-5 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
912 | _aZDB-2-LNC | ||
942 | _cSPRINGER | ||
999 |
_c179188 _d179188 |