Cantitate/Preț
Produs
Total produse
Vezi coșul
Pattern Recognition: 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings de Christian Bauckhage

Pattern Recognition: 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings: Lecture Notes in Computer Science, cartea 13024

Editat de Christian Bauckhage, Juergen Gall, Alexander Schwing
en Limba Engleză Paperback – 14 ian 2022
This 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.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 69646 lei

Preț vechi: 87057 lei
-20%

Puncte Express: 1045
Preț estimativ în valută:
12312€ • 14767$ • 10703£

Carte tipărită la comandă

Livrare economică 13-27 martie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9783030926588
ISBN-10: 3030926583
Pagini: 726
Ilustrații: XVII, 726 p. 98 illus.
Dimensiuni: 155 x 235 mm
Greutate: 1.03 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

Machine 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.