Del 15297 i serien Lecture Notes in Computer Science

Pattern Recognition

46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10–13, 2024, Proceedings, Part I

Häftad, Engelska, 2025

AvDaniel Cremers,Zorah Lähner,Michael Moeller,Matthias Nießner,Björn Ommer,Rudolph Triebel

1 089 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2025-04-23
Mått155 x 235 x 21 mm
Vikt581 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor365
FörlagSpringer International Publishing AG
ISBN9783031851803

Tillhör följande kategorier

Tillämpad datateknik inom Data och IT
Systemvetenskap och AI inom Data och IT

.- 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.