Pattern Recognition

47th DAGM German Conference, DAGM GCPR 2025, Freiburg, Germany, September 23–26, 2025, Proceedings

Margret Keuper, Francesco Locatello (Herausgeber)

Buch | Softcover

XIII, 644 Seiten

2026
Springer International Publishing (Verlag)
978-3-032-12839-3 (ISBN)

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This book constitutes the refereed proceedings of the 47th DAGM German Conference on Pattern Recognition, DAGM-GCPR 2025, held in Freiburg, Germany, during September 23-26, 2025.

The 40 full papers presented in this volume were carefully reviewed and selected from 85 submissions.

They are grouped into the following topics: Computer Vision Systems and Applications; Video Analysis and Synthesis; Machine Learning Methods; Applications of Foundation Models; Safety and Robustness; 3D Perception and Reconstruction; Photogrammetry and Remote Sensing.

.- Computer Vision Systems and Applications.

.- Box it and Track it: A Weakly Supervised Framework for Cell Tracking.

.- A Cascaded Dilated Convolution Approach for Mpox Lesion Classification.

.- HistDiST: Histopathological Diffusion-based Stain Transfer.

.- Deep Learning-Assisted Dynamic Mode Decomposition for Non-resonant Background Removal in CARS Spectroscopy.

.- -Quant: Towards Learnable Quantization for Low-bit Pattern
Recognition.

.- EVCS: A Benchmark for Fine-Grained Electric Vehicle Charging
Station Detection.

.- Video Analysis and Synthesis.

.- SegSLR: Promptable Video Segmentation for Isolated Sign Language
Recognition.

.- Video Object Segmentation-aware Audio Generation.

.- MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices.

.- VisualChef: Generating Visual Aids in Cooking via Mask Inpainting.

.- StorySync: Training-Free Subject Consistency via Region Harmonization.

.- Structured Universal Adversarial Attacks on Object Detection for
Video Sequences.

.- Road Obstacle Video Segmentation.

.- Machine Learning Methods.

.- Don t Miss Out on Novelty: Importance of Novel Features for Deep
Anomaly Detection.

.- LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain
Translation.

.- On the Dangers of Bootstrapping Generation for Continual Learning
and Beyond.

.- Combined Image Data Augmentations diminish the benefits of
Adaptive Label Smoothing.

.- Efficient Masked Attention Transformer for Few-Shot Classification
and Segmentation.

.- Applications of Foundation Models.

.- Using Knowledge Graphs to harvest datasets for efficient CLIP model
training.

.- Unlocking In-Context Learning for Natural Datasets Beyond Language
Modelling.

.- Investigating Structural Pruning and Recovery Techniques for
Compressing Multimodal Large Language Models: An Empirical Study.

.- Assessing Foundation Models for Mold Colony Detection with Limited
Training Data.

.- Common Data Properties Limit Object-Attribute Binding in CLIP.

.- subCellSAM: Zero-Shot (Sub-)Cellular Segmentation for Hit Validation
in Drug Discovery.

.- Safety and Robustness.

.- synth-dacl: Does Synthetic Defect Data Enhance Segmentation
Accuracy and Robustness for Real-World Bridge Inspections?.

.- FedPCE: Federated Personalized Client Embeddings for Post-training
Knowledge Distillation.

.- Object Risk Estimation for Autonomous Driving Safety.

.- Rethinking Semi-supervised Segmentation Beyond Accuracy:
Robustness and Reliability.

.- Detection of Synthetic Face Images: Accuracy, Robustness, Generalization.

.- 3D Perception and Reconstruction.

.- MT-Occ: Single-View 3D Occupancy Prediction via Multi-Task Distillation.

.- Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D
Semantic Segmentation.

.- CoProU-VO: Combining Projected Uncertainty for End-to-End
Unsupervised Monocular Visual Odometry.

.- Combining Absolute and Semi-Generalized Relative Poses for Visual
Localization.

.- Graph Roof Reconstruction with Synthetic Data from Misaligned Labels.

.- sshELF: Single-Shot Hierarchical Extrapolation of Latent Features for
3D Reconstruction from Sparse-Views.

.- Photogrammetry and Remote Sensing.

.- NaT-ReX: Naturalness Assessment with Transformer-Based Reliable
Explainability.

.- Semantic Segmentation of Structural Da

Erscheint lt. Verlag	19.1.2026
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XIII, 644 p. 190 illus.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
Schlagworte	3D Reconstruction • Artificial Intelligence • computer vision • Deep learning • Health Informatics • Image Compression • Information Retrieval • Knowledge Representation and Reasoning • machine learning algorithms • machine learning theory • Mathematical Optimization • Natural and Life Sciences • neural rendering • pattern recognition • Photogrammetry • Remote Sensing • Robotics • robust models • Shape modeling • spatial-temporal systems
ISBN-10	3-032-12839-0 / 3032128390
ISBN-13	978-3-032-12839-3 / 9783032128393
Zustand	Neuware