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Einband grossComputer Vision - ECCV 2024
ISBN/GTIN

Computer Vision - ECCV 2024

18th European Conference, Milan, Italy, September 29-October 4, 2024, Proceedings, Part III
BuchKartoniert, Paperback
463 Seiten
Englisch
Springererscheint am26.10.20242024
The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29-October 4, 2024.The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR87,73
BuchKartoniert, Paperback
EUR79,17
BuchKartoniert, Paperback
EUR79,17
BuchKartoniert, Paperback
EUR87,73
BuchKartoniert, Paperback
EUR87,73
BuchKartoniert, Paperback
EUR79,17
BuchKartoniert, Paperback
EUR79,17
BuchKartoniert, Paperback
EUR79,17
BuchKartoniert, Paperback
EUR87,73

Produkt

KlappentextThe multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29-October 4, 2024.The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.
Details
ISBN/GTIN978-3-031-72645-3
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2024
Erscheinungsdatum26.10.2024
Auflage2024
ReiheLecture Notes in Computer Science
Seiten463 Seiten
SpracheEnglisch
IllustrationenApprox. 460 p.
Artikel-Nr.17323374

Inhalt/Kritik

Inhaltsverzeichnis
Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal.- Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation.- Efficient Few-Shot Action Recognition via Multi-Level Post-Reasoning.- Text2Place: Affordance-aware Text Guided Human Placement.- OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations.- Zero-Shot Multi-Object Scene Completion.- Beta-Tuned Timestep Diffusion Model.- POA: Pre-training Once for Models of All Sizes.- Taming Latent Diffusion Model for Neural Radiance Field Inpainting.- MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation.- ByteEdit: Boost, Comply and Accelerate Generative Image Editing.- ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic Fusion.- High-Resolution and Few-shot View Synthesis from Asymmetric Dual-lens Inputs.- Accelerating Image Super-Resolution Networks with Pixel-Level Classification.- LASS3D: Language-Assisted Semi-Supervised 3D Semantic Segmentation with Progressive Unreliable Data Exploitation.- Contourlet Residual for Prompt Learning Enhanced Infrared Image Super-Resolution.- Click-Gaussian: Interactive Segmentation to Any 3D Gaussians.- Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex Driving Scenes.- DySeT: a Dynamic Masked Self-distillation Approach for Robust Trajectory Prediction.- Track Everything Everywhere Fast and Robustly.- Towards Open-ended Visual Quality Comparison.- FreeInit: Bridging Initialization Gap in Video Diffusion Models.- DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs.- Eliminating Feature Ambiguity for Few-Shot Segmentation.- Soft Prompt Generation for Domain Generalization.- Shedding More Light on Robust Classifiers under the lens of Energy-based Models.mehr

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