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

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 12362. | LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.Publication details: Cham : Springer, 2020.Description: 1 online resource (845 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030585204
  • 3030585204
Other title:
  • ECCV 2020
Subject(s): Genre/Form: Additional physical formats: Print version:: Computer Vision - ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XVII.DDC classification:
  • 006.3/7 23
  • 006.37
LOC classification:
  • TA1634
Online resources:
Contents:
Intro -- Foreword -- Preface -- Organization -- Contents -- Part XVII -- Class-Wise Dynamic Graph Convolution for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Preliminaries -- 3.2 Overall Framework -- 3.3 Class-Wise Dynamic Graph Convolution Module -- 3.4 Loss Function -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparisons with State-of-the-Arts -- 5 Conclusions -- References -- Character-Preserving Coherent Story Visualization -- 1 Introduction -- 2 Related Work
2.1 GAN-based Text-to-Image Synthesis -- 2.2 Evaluation Metrics of Image Generation -- 3 Character-Preserving Coherent Story Visualization -- 3.1 Overview -- 3.2 Story and Context Encoder -- 3.3 Figure-Ground Aware Generation -- 3.4 Loss Function -- 3.5 Fréchet Story Distance -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Dataset -- 4.3 Baselines -- 4.4 Qualitative Comparison -- 4.5 Quantitative Comparison -- 4.6 Architecture Search -- 4.7 FSD Analysis -- 5 Conclusions -- References -- GINet: Graph Interaction Network for Scene Parsing -- 1 Introduction -- 2 Related Work
3 Approach -- 3.1 Framework of Graph Interaction Network (GINet) -- 3.2 Graph Interaction Unit -- 3.3 Semantic Context Loss -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experiments on Pascal-Context -- 4.4 Experiments on COCO Stuff -- 4.5 Experiments on ADE20K -- 5 Conclusion -- References -- Tensor Low-Rank Reconstruction for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Tensor Generation Module -- 3.3 Tensor Reconstruction Module -- 3.4 Global Pooling Module -- 3.5 Network Details -- 3.6 Relation to Previous Approaches
4 Experiments -- 4.1 Implementation Details -- 4.2 Results on Different Datasets -- 4.3 Ablation Study -- 4.4 Further Discussion -- 5 Conclusion -- References -- Attentive Normalization -- 1 Introduction -- 2 Related Work -- 3 The Proposed Attentive Normalization -- 3.1 Background on Feature Normalization -- 3.2 Background on Feature Attention -- 3.3 Attentive Normalization -- 4 Experiments -- 4.1 Ablation Study -- 4.2 Image Classification in ImageNet-1000 -- 4.3 Object Detection and Segmentation in COCO -- 5 Conclusion -- References -- Count- and Similarity-Aware R-CNN for Pedestrian Detection
1 Introduction -- 2 Related Work -- 3 Baseline Two-Stage Detection Framework -- 4 Our Approach -- 4.1 Detection Branch -- 4.2 Count-and-Similarity Branch -- 4.3 Inference -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Implementation Details -- 5.3 CityPersons Dataset -- 5.4 CrowdHuman Dataset -- 5.5 Results on Person Instance Segmentation -- 6 Conclusion -- References -- TRADI: Tracking Deep Neural Network Weight Distributions -- 1 Introduction -- 2 TRAcking of the Weight DIstribution (TRADI) -- 2.1 Notations and Hypotheses
Summary: 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. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers 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; object recognition; motion estimation.
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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. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers 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; object recognition; motion estimation.

Intro -- Foreword -- Preface -- Organization -- Contents -- Part XVII -- Class-Wise Dynamic Graph Convolution for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Preliminaries -- 3.2 Overall Framework -- 3.3 Class-Wise Dynamic Graph Convolution Module -- 3.4 Loss Function -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparisons with State-of-the-Arts -- 5 Conclusions -- References -- Character-Preserving Coherent Story Visualization -- 1 Introduction -- 2 Related Work

2.1 GAN-based Text-to-Image Synthesis -- 2.2 Evaluation Metrics of Image Generation -- 3 Character-Preserving Coherent Story Visualization -- 3.1 Overview -- 3.2 Story and Context Encoder -- 3.3 Figure-Ground Aware Generation -- 3.4 Loss Function -- 3.5 Fréchet Story Distance -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Dataset -- 4.3 Baselines -- 4.4 Qualitative Comparison -- 4.5 Quantitative Comparison -- 4.6 Architecture Search -- 4.7 FSD Analysis -- 5 Conclusions -- References -- GINet: Graph Interaction Network for Scene Parsing -- 1 Introduction -- 2 Related Work

3 Approach -- 3.1 Framework of Graph Interaction Network (GINet) -- 3.2 Graph Interaction Unit -- 3.3 Semantic Context Loss -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experiments on Pascal-Context -- 4.4 Experiments on COCO Stuff -- 4.5 Experiments on ADE20K -- 5 Conclusion -- References -- Tensor Low-Rank Reconstruction for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Tensor Generation Module -- 3.3 Tensor Reconstruction Module -- 3.4 Global Pooling Module -- 3.5 Network Details -- 3.6 Relation to Previous Approaches

4 Experiments -- 4.1 Implementation Details -- 4.2 Results on Different Datasets -- 4.3 Ablation Study -- 4.4 Further Discussion -- 5 Conclusion -- References -- Attentive Normalization -- 1 Introduction -- 2 Related Work -- 3 The Proposed Attentive Normalization -- 3.1 Background on Feature Normalization -- 3.2 Background on Feature Attention -- 3.3 Attentive Normalization -- 4 Experiments -- 4.1 Ablation Study -- 4.2 Image Classification in ImageNet-1000 -- 4.3 Object Detection and Segmentation in COCO -- 5 Conclusion -- References -- Count- and Similarity-Aware R-CNN for Pedestrian Detection

1 Introduction -- 2 Related Work -- 3 Baseline Two-Stage Detection Framework -- 4 Our Approach -- 4.1 Detection Branch -- 4.2 Count-and-Similarity Branch -- 4.3 Inference -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Implementation Details -- 5.3 CityPersons Dataset -- 5.4 CrowdHuman Dataset -- 5.5 Results on Person Instance Segmentation -- 6 Conclusion -- References -- TRADI: Tracking Deep Neural Network Weight Distributions -- 1 Introduction -- 2 TRAcking of the Weight DIstribution (TRADI) -- 2.1 Notations and Hypotheses

Online resource; title from PDF title page (SpringerLink, viewed February 3, 2021).

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