Pattern Recognition

43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings

Häftad, Engelska, 2022

Av Christian Bauckhage, Juergen Gall, Alexander Schwing

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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.

Produktinformation

Utgivningsdatum2022-01-14
Mått155 x 235 x 40 mm
Vikt1 107 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor726
FörlagSpringer Nature Switzerland AG
ISBN9783030926588

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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.