Cremers / Lähner / Moeller Pattern Recognition

46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10–13, 2024, Proceedings, Part I
Erscheinungsjahr 2025
ISBN: 978-3-031-85181-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 365 Seiten

Reihe: Lecture Notes in Computer Science

This 2-volume set LNCS 15297-15298 constitutes the refereed proceedings of the 46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024, held in Munich, Germany, during September 10-13, 2024.
The 44 full papers included in these proceedings were carefully reviewed and selected from 81 submissions. They are organized in these topical sections:
Part I: Clustering and Segmentation; Learning Techniques; Medical and Biological Applications; Uncertainty and Explainability.
Part II: Modelling of Faces and Shapes; Image Generation and Reconstruction; 3D Analysis and Sythesis; Video Analysis; Photogrammetry and Remote Sensing.

Zielgruppe

Research

Autoren/Hrsg.

Cremers, Daniel

Lähner, Zorah

Moeller, Michael

Nießner, Matthias

Ommer, Björn

Triebel, Rudolph

Inhaltsverzeichnis

.- Clustering and Segmentation.

.- PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks.

.- A State-of-the-Art Cutting Plane Algorithm for Clique Partitioning.

.- Self-Supervised Semantic Segmentation from Audio-Visual Data.

.- BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation.

.- Learning Techniques.

.- FullCert: Deterministic End-to-End Certification for Training and Inference of Neural Networks.

.- Self-Masking Networks for Unsupervised Adaptation.

.- A Theoretical Formulation on the Use of Multiple Positive Views in Contrastive Learning

.- Decoupling of neural network calibration measures.

.- Examining Common Paradigms in Multi-Task Learning.

.- DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning.

.- Efficient and Discriminative Image Feature Extraction for Universal Image Retrieval ..

.- Anomaly Detection with Conditioned Denoising Diffusion Models.

.- Medical and Biological Applications.

.- SurgeoNet: Realtime 3D Pose Estimation of Articulated Surgical Instruments from Stereo Images using a Synthetically-trained Network.

.- Foundation Models Permit Retinal Layer Segmentation Across OCT Devices.

.- Correlation Clustering of Organoid Images.

.- Animal Identification with Independent Foreground and Background Modeling.

.- Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks.

.- Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction.

.- Uncertainty and Explainability.

.- Latent Diffusion Counterfactual Explanations.

.- Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations.

.- Uncertainty Voting Ensemble for Imbalanced Deep Regression.

.- Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning.