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  • Produktbild: Computer Vision – ECCV 2022
  • Produktbild: Computer Vision – ECCV 2022
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Computer Vision – ECCV 2022 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI

Aus der Reihe Lecture Notes in Computer Science
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104,99 € UVP 120,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2022

Abbildungen

LVII, 755 p. 199 illus., 190 illus. in color.

Herausgeber

Shai Avidan + weitere

Verlag

Springer

Seitenzahl

755

Maße (L/B/H)

23,5/15,5/4,4 cm

Gewicht

1212 g

Auflage

1st ed. 2022

Sprache

Englisch

ISBN

978-3-031-19802-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2022

Abbildungen

LVII, 755 p. 199 illus., 190 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

755

Maße (L/B/H)

23,5/15,5/4,4 cm

Gewicht

1212 g

Auflage

1st ed. 2022

Sprache

Englisch

ISBN

978-3-031-19802-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Computer Vision – ECCV 2022
  • Produktbild: Computer Vision – ECCV 2022
  • Active Label Correction Using Robust Parameter Update and Entropy Propagation.- Unpaired Image Translation via Vector Symbolic Architectures.- UniNet: Unified Architecture Search with Convolution, Transformer, and MLP.- AMixer: Adaptive Weight Mixing for Self-Attention Free Vision Transformers.- TinyViT: Fast Pretraining Distillation for Small Vision Transformers.- Equivariant Hypergraph Neural Networks.- ScaleNet: Searching for the Model to Scale.- Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction.- ViTAS: Vision Transformer Architecture Search.- LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds.- Uncertainty-DTW for Time Series and Sequences.- Black-Box Few-Shot Knowledge Distillation.- Revisiting Batch Norm Initialization.- SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling.- Filter Pruning via Feature Discrimination in Deep Neural Networks.- LA3: Efficient Label-Aware AutoAugment.- Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps.- BA-Net: Bridge Attention for Deep Convolutional Neural Networks.- SAU: Smooth Activation Function Using Convolution with Approximate Identities.- Multi-Exit Semantic Segmentation Networks.- Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks.- PointScatter: Point Set Representation for Tubular Structure Extraction.- Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection.- Graph-Constrained Contrastive Regularization for Semi-Weakly Volumetric Segmentation.- Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration.- Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation.- Personalizing Federated Medical Image Segmentation via Local Calibration.- One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement.- Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization.- Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation.- ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images.- CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.- UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier.- DLME: Deep Local-Flatness Manifold Embedding.- Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching.- Graph Neural Network for Cell Tracking in Microscopy Videos.- CXR Segmentation by AdaIN-Based Domain Adaptation and Knowledge Distillation.- Accurate Detection of Proteins in Cryo-Electron Tomograms from Sparse Labels.- K-SALSA: K-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment.- RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-Guided Disease Classification.- Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images.- Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis.- Towards Grand Unification of Object Tracking.