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[null,null,"Titolo",null,"Pattern Recognition : 46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10–13, 2024, Proceedings, Part I / / edited by Daniel Cremers, Zorah Lähner, Michael Moeller, Matthias Nießner, Björn Ommer, Rudolph Triebel"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-85181-1"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVII, 365 p. 113 illus., 103 illus. in color.)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15297"]
[null,null,"Disciplina",null,"006"]
[null,null,"Soggetti",null,"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer systems"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer System Implementation"]
[{"$nulls":4},"Computers and Education"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- 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 "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","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."]