Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings. Part XVIII

Computer vision -- ECCV 2020 : Part XVIII / 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings. by Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm (eds.).

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic....

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Bibliographic Details
Corporate Author: European Conference on Computer Vision Online
Other Authors: Vedaldi, Andrea (Editor), Bischof, Horst (Editor), Brox, Thomas (Editor), Frahm, Jan-Michael (Editor)
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, [2020]
Series:Lecture notes in computer science ; 12363.
LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics.
Subjects:
Computer vision > Congresses.
Optical data processing.
Application software.
Machine learning.
Computer organization.
Pattern perception.
Computer vision
Application software
Computer organization
Machine learning
Optical data processing
Pattern perception
Electronic books.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Foreword
  • Preface
  • Organization
  • Contents
  • Part XVIII
  • 3D Bird Reconstruction: A Dataset, Model, and Shape Recovery from a Single View
  • 1 Introduction
  • 2 Related Work
  • 3 Approach
  • 4 The Cowbird Dataset
  • 5 Experiments
  • 6 Conclusions
  • References
  • We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos*-6pt
  • 1 Introduction
  • 2 Related Work
  • 3 A Dataset for Relational Event Abstraction
  • 3.1 Human Performance on Event Abstraction
  • 4 Approach
  • 4.1 Relational Set Abstraction Model
  • 4.2 Learning
  • 5 Experiments
  • 5.1 Recognizing Set Abstractions
  • 5.2 Set Completion
  • 5.3 Finding the Odd One Out
  • 6 Conclusion
  • References
  • Joint Optimization for Multi-person Shape Models from Markerless 3D-Scans
  • 1 Introduction
  • 2 Related Work
  • 2.1 Human Shape Models
  • 2.2 Subdivision Surfaces
  • 2.3 Joint Optimization
  • 3 Articulated Morphable Shape Model
  • 4 Objective and Optimization
  • 5 Experimental Evaluation
  • 5.1 Training
  • 5.2 Inference
  • 6 Discussion and Conclusion
  • References
  • Accurate RGB-D Salient Object Detection via Collaborative Learning
  • 1 Introduction
  • 2 Related Work
  • 3 Collaborative Learning Framework
  • 3.1 The Overall Architecture
  • 3.2 Feature Preprocessing
  • 3.3 Collaborative Learning
  • 4 Experiments
  • 4.1 Dataset
  • 4.2 Experimental Setup
  • 4.3 Ablation Analysis
  • 4.4 Comparison with State-of-the-Arts
  • 5 Conclusion
  • References
  • Finding Your (3D) Center: 3D Object Detection Using a Learned Loss
  • 1 Introduction
  • 2 PreviousWork
  • 3 OurApproach
  • 3.1 Training
  • 3.2 Network
  • 4 Evaluation
  • 4.1 Network
  • 4.2 Results
  • 5 Discussion
  • 6 Conclusion
  • References
  • Collaborative Training Between Region Proposal Localization and Classification for Domain Adaptive Object Detection
  • 1 Introduction
  • 2 Related Works
  • 3 Method
  • 3.1 Framework Overview
  • 3.2 Collaborative Self-training
  • 3.3 Maximize Discrepancy Classifier on Detectors
  • 3.4 RPN Weighted Alignment
  • 3.5 Overall Objective
  • 4 Experiment
  • 4.1 Implement Details
  • 4.2 Domain Adaptation for Detection
  • 5 Ablation Study
  • 6 Conclusion
  • References
  • Two Stream Active Query Suggestion for Active Learning in Connectomics
  • 1 Introduction
  • 2 Related Work
  • 3 Active Learning Framework Overview
  • 4 Two-Stream Active Query Suggestion
  • 4.1 Two-Stream Clustering
  • 4.2 Active Clustering
  • 4.3 Learning Strategy
  • 5 EM-R50 Connectomics Dataset
  • 6 Experiments on Connectomics Datasets
  • 6.1 Comparing with State-of-the-Art Methods
  • 6.2 Ablation Analysis of Two-Stream Active Query Suggestion
  • 6.3 Ablation Analysis of Active Learning Pipeline
  • 7 Application to Natural Image Classification
  • 8 Conclusion
  • References
  • Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
  • 1 Introduction
  • 2 Related Work
  • 3 Preliminaries

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