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

Computer Vision -- ECCV 2020 : Part X / 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings. 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 : Springer, 2020.
Series:Lecture notes in computer science ; 12355.
LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics.
Subjects:
Computer vision > Congresses.
Optical data processing.
Computer vision
Optical data processing
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 X
  • Discriminability Distillation in Group Representation Learning
  • 1 Introduction
  • 2 Related Work
  • 3 Discriminability Distillation Learning
  • 3.1 Formulation of Group Representation Learning
  • 3.2 Formulation of Discriminability
  • 3.3 Discriminability Distillation Learning
  • 3.4 Feature Aggregation G
  • 3.5 Advantage of Discriminability Distillation Learning
  • 4 Experiments
  • 4.1 Set-to-Set Face Recognition
  • 4.2 Video-Based Person Re-identification
  • 4.3 Action Recognition
  • 5 Conclusion
  • References
  • Monocular Expressive Body Regression Through Body-Driven Attention
  • 1 Introduction
  • 2 Related Work
  • 3 Method
  • 3.1 3D Body Representation
  • 3.2 Body-Driven Attention
  • 3.3 Implementation Details
  • 4 Experiments
  • 4.1 Evaluation Datasets
  • 4.2 Evaluation Metrics
  • 4.3 Quantitative and Qualitative Experiments
  • 5 Conclusion
  • References
  • Dual Adversarial Network: Toward Real-World Noise Removal and Noise Generation
  • 1 Introduction
  • 2 Related Work
  • 2.1 Noise Removal
  • 2.2 Noise Generation
  • 3 Proposed Method
  • 3.1 Two Factorizations of Joint Distribution
  • 3.2 Dual Adversarial Model
  • 3.3 Training Strategy
  • 3.4 Network Architecture
  • 4 Evaluation Metrics
  • 5 Experimental Results
  • 5.1 Experimental Settings
  • 5.2 Results on SIDD Benchmark
  • 5.3 Results on DND and Nam Benchmarks
  • 6 Conclusion
  • References
  • Linguistic Structure Guided Context Modeling for Referring Image Segmentation
  • 1 Introduction
  • 2 Related Work
  • 2.1 Semantic Segmentation
  • 2.2 Referring Image Localization and Segmentation
  • 2.3 Structural Context Modeling
  • 3 Method
  • 3.1 Multimodal Feature Extraction
  • 3.2 Linguistic Structure Guided Context Modeling
  • 3.3 Dual-Path Multi-Level Feature Fusion
  • 4 Experiments
  • 4.1 Experimental Setting
  • 4.2 Comparison with State-of-the-Arts
  • 4.3 Ablation Studies
  • 5 Conclusion and Future Work
  • References
  • Federated Visual Classification with Real-World Data Distribution
  • 1 Introduction
  • 2 Related Work
  • 3 Federated Visual Classification Problems
  • 4 Datasets
  • 4.1 INaturalist-User-120k and iNaturalist-Geo Splits
  • 4.2 Landmarks-User-160k
  • 5 Methods
  • 5.1 Federated Averaging and Server Momentum
  • 5.2 Importance Reweighted Client Objectives
  • 5.3 Splitting Imbalanced Clients with Virtual Clients
  • 6 Experiments
  • 6.1 Classification Accuracy Vs Distribution Non-Identicalness
  • 6.2 Importance Reweighting
  • 6.3 Federated Virtual Clients
  • 6.4 Federated Visual Classification Benchmarks
  • 6.5 Implementation Details
  • 7 Conclusions
  • References
  • Robust Re-Identification by Multiple Views Knowledge Distillation
  • 1 Introduction
  • 2 Related Works
  • 3 Method
  • 3.1 Teacher Network
  • 3.2 Views Knowledge Distillation (VKD)
  • 4 Experiments
  • 4.1 Datasets
  • 4.2 Self-distillation
  • 4.3 Comparison with State-Of-The-Art

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