Produktbild: Computer Vision – ECCV 2022
Band 13689 - 13%

Computer Vision – ECCV 2022 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXIX

13% sparen

104,99 € UVP 120,99 €

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.10.2022

Herausgeber

Shai Avidan + weitere

Verlag

Springer

Seitenzahl

757

Maße (L/B/H)

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

Gewicht

1212 g

Auflage

1st edition 2022

Sprache

Englisch

ISBN

978-3-031-19817-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.10.2022

Herausgeber

Verlag

Springer

Seitenzahl

757

Maße (L/B/H)

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

Gewicht

1212 g

Auflage

1st edition 2022

Sprache

Englisch

ISBN

978-3-031-19817-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)

  • Produktbild: Computer Vision – ECCV 2022
  • ¿Box-Supervised Instance Segmentation with Level Set Evolution.- Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding.- Adaptive Agent Transformer for Few-Shot Segmentation.- Waymo Open Dataset: Panoramic Video Panoptic Segmentation.- TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation.- AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-Shot Interactions.- Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation.- Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications.- Perceptual Artifacts Localization for Inpainting.- 2D Amodal Instance Segmentation Guided by 3D Shape Prior.- Data Efficient 3D Learner via Knowledge Transferred from 2D Model.- Adaptive Spatial-BCE Loss for Weakly Supervised Semantic Segmentation.- Dense Gaussian Processes for Few-Shot Segmentation.- 3D Instances as 1D Kernels.- TransMatting: Enhancing Transparent Objects Matting with Transformers.- MVSalNet:Multi-View Augmentation for RGB-D Salient Object Detection.- k-Means Mask Transformer.- SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness.- Adversarial Erasing Framework via Triplet with Gated Pyramid Pooling Layer for Weakly Supervised Semantic Segmentation.- Continual Semantic Segmentation via Structure Preserving and Projected Feature Alignment.- Interclass Prototype Relation for Few-Shot Segmentation.- Slim Scissors: Segmenting Thin Object from Synthetic Background.- Abstracting Sketches through Simple Primitives.- Multi-Scale and Cross-Scale Contrastive Learning for Semantic Segmentation.- One-Trimap Video Matting.- D2ADA: Dynamic Density-Aware Active Domain Adaptation for Semantic Segmentation.- Learning Quality-Aware Dynamic Memory for Video Object Segmentation.- Learning Implicit Feature Alignment Function for Semantic Segmentation.- Quantum Motion Segmentation.- Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation.- Laplacian Mesh Transformer: Dual Attention and Topology Aware Network for 3D Mesh Classification and Segmentation.- Geodesic-Former: A Geodesic-Guided Few-Shot 3D Point Cloud Instance Segmenter.- Union-Set Multi-source Model Adaptation for Semantic Segmentation.- Point MixSwap: Attentional Point Cloud Mixing via Swapping Matched Structural Divisions.- BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation.- SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection.- Global Spectral Filter Memory Network for Video Object Segmentation.- Video Instance Segmentation via Multi-Scale Spatio-Temporal Split Attention Transformer.- RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation.- Learning Topological Interactions for Multi-Class Medical Image Segmentation.- Unsupervised Segmentation in Real-World Images via Spelke Object Inference.- A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model.