01847nam0 2200385 i 450 VEA007271820231121125920.02728303312IT96-8482 19950919d1995 ||||0itac50 bafreitz01i xxxe z01nTessera frumentariales procédures de distribution du blé public à Rome à la fin de la république et au début de l'empirepar Catherine Virlouvet\Roma!Ècole française de Rome1995424 p., \12! c. di tav.ill.25 cm.Bibliothèque des écoles françaises d'Athènes et de Rome286001CFI00312232001 Bibliothèque des écoles françaises d'Athènes et de Rome28671202École française de RomeCFIV017404GranoDistribuzioneRoma anticaSec. 3. a.C. -1. d.C.FIRRMLC418806IGeneri alimentariApprovvigionamentoRoma anticaSec. 3. a.C. -1. d.C.FIRRMLC418807IAnnona romanaSec. 3. a.C. -1. d.C.FIRRMLC418808IGranoDistribuzioneRoma anticaFIRRMLC049429I363.856093721Virlouvet, CatherineCFIV126734070158815ITIT-0119950919IT-RM028 IT-RM0313 IT-FR0017 Biblioteca Universitaria AlessandrinaRM028 BIBLIOTECA CASANATENSERM0313 Biblioteca umanistica Giorgio ApreaFR0017 NVEA0072718Biblioteca umanistica Giorgio Aprea 52MAG 1 Coll L-BEFAR 286 52FLS0000318505 VMN RS A 2017012420170124 01 07 52Tessera frumentaria736577UNICAS07150nam 22007815 450 991052007370332120250428163143.03-030-92659-110.1007/978-3-030-92659-5(MiAaPQ)EBC6855006(Au-PeEL)EBL6855006(CKB)20667342000041(PPN)260307769(DE-He213)978-3-030-92659-5(EXLCZ)992066734200004120220113d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPattern Recognition 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings /edited by Christian Bauckhage, Juergen Gall, Alexander Schwing1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (734 pages)Image Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;13024Print version: Bauckhage, Christian Pattern Recognition Cham : Springer International Publishing AG,c2022 9783030926588 Machine Learning and Optimization -- Sublabel-Accurate Multilabeling Meets Product Label Spaces -- InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization -- Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise -- Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data -- Revisiting Consistency Regularization for Semi-Supervised Learning -- Learning Robust Models Using the Principle of Independent Causal Mechanisms -- Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks -- Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators -- End-to-end Learning of Fisher Vector Encodings for Part Features in Fine-grained Recognition -- Investigating the Consistency of Uncertainty Sampling in Deep Active Learning -- ScaleNet: An Unsupervised Representation Learning Method for Limited Information -- Actions, Events, and Segmentation -- A New Split for Evaluating True Zero-Shot Action Recognition -- Video Instance Segmentation with Recurrent Graph Neural Networks -- Distractor-Aware Video Object Segmentation -- (SP)^2Net for Generalized Zero-Label Semantic Segmentation -- Contrastive Representation Learning for Hand Shape Estimation -- Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks -- FIFA: Fast Inference Approximation for Action Segmentation -- Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision -- A Comparative Study of PnP and Learning Approaches to Super-Resolution in a Real-World Setting -- Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing -- Spatiotemporal Outdoor Lighting Aggregation on Image Sequences -- Generative Models and Multimodal Data -- AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style -- Learning Conditional Invariance through Cycle Consistency -- CAGAN: Text-To-Image Generation with Combined Attention Generative Adversarial Networks -- TxT: Crossmodal End-to-End Learning with Transformers -- Diverse Image Captioning with Grounded Style -- Labeling and Self-Supervised Learning -- Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-Labeling -- Quantifying Uncertainty of Image Labelings Using Assignment Flows -- Implicit and Explicit Attention for Zero-Shot Learning -- Self-Supervised Learning for Object Detection in Autonomous Driving -- Assignment Flows and Nonlocal PDEs on Graphs -- Applications -- Viewpoint-Tolerant Semantic Segmentation for Aerial Logistics -- T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression -- TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases -- Detecting Slag Formations with Deep Convolutional Neural Networks -- Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture -- Weakly Supervised Segmentation Pre-training for Plant Cover Prediction -- How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation? -- 3D Modeling and Reconstruction -- Clustering Persistent Scatterer Points Based on a Hybrid Distance Metric -- CATEGORISE: An Automated Framework for Utilizing the Workforce of the Crowd for Semantic Segmentation of 3D Point Clouds -- Zero-Shot remote sensing image super resolution based on image continuity and self-tessellations -- A Comparative Survey of Geometric Light Source Calibration Methods -- Quantifying point cloud realism through adversarially learned latent representations -- Full-Glow: Fully conditional Glow for more realistic image generation -- Multidirectional Conjugate Gradients for Scalable Bundle Adjustment. .This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic. The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.Image Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;13024Pattern recognition systemsMachine learningComputer visionComputer engineeringComputer networksSocial sciencesData processingEducationData processingAutomated Pattern RecognitionMachine LearningComputer VisionComputer Engineering and NetworksComputer Application in Social and Behavioral SciencesComputers and EducationPattern recognition systems.Machine learning.Computer vision.Computer engineering.Computer networks.Social sciencesData processing.EducationData processing.Automated Pattern Recognition.Machine Learning.Computer Vision.Computer Engineering and Networks.Computer Application in Social and Behavioral Sciences.Computers and Education.006.4006.4Bauckhage ChristianGall JuergenSchwing AlexanderMiAaPQMiAaPQMiAaPQBOOK9910520073703321Pattern Recognition381471UNINA