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.).

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