Full Description
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.
Contents
.- 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.
.- γ-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 Damage: A Comparative Study
of YOLO11 and Encoder-Decoder Networks.
.- Can Multitask Learning Enhance Model Explainability?.
.- Out-of-Distribution Detection in LiDAR Semantic Segmentation Using
Epistemic Uncertainty from Hierarchical GMMs.
.- RadarSeq: A Temporal Vision Framework for User Churn Prediction
via Radar Sequence Chart.
