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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.
List of contents
.- 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.
Summary
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.
