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

Intro
Foreword
Preface
Organization
Contents
Part XXV
Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation
1 Introduction
2 Related Work
3 Preliminaries
3.1 Operations
3.2 Search Space
4 Faster AutoAugment
4.1 Differentiable Data Augmentation Pipeline
4.2 Data Augmentation as Density Matching
5 Experiments and Results
5.1 Experimental Details
5.2 Results
6 Analysis
7 Conclusion
References
Hand-Transformer: Non-Autoregressive Structured Modeling for 3D Hand Pose Estimation
1 Introduction
2 Related Work

3 Methodology
3.1 Tranformer Revisited
3.2 Non-Autoregressive Structured Decoding
3.3 Encoder
3.4 End-to-End Training
4 Experiments
4.1 Datasets
4.2 Evaluation Metrics
4.3 Implementation Details
4.4 Ablation Study
4.5 Comparisons with the State-of-the-Arts
5 Conclusion
References
Boundary-Aware Cascade Networks for Temporal Action Segmentation
1 Introduction
2 Related Work
3 Boundary-Aware Cascade Networks
3.1 Video Encoding
3.2 Stage Cascade
3.3 Local Barrier Pooling
3.4 Training BCN
4 Experiments
4.1 Study on SC and LBP

4.2 Ablation Study on Hyper-parameters
4.3 Comparison with the State of the Art
5 Conclusion
References
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
1 Introduction
2 Related Work
3 Methods
3.1 Reference Pool
3.2 Local Feature Enhancement Module
3.3 Loss Function
3.4 Network Architecture
4 Experiment Results
4.1 Dataset
4.2 Implementation Details
4.3 Quantitative Evaluation
4.4 Qualitative Evaluation
4.5 Ablation Study
5 Conclusion
References

Inference Graphs for CNN Interpretation
1 Introduction
2 Related Work
3 Method
3.1 Inference Graphs for MLPs
3.2 Inference Graphs for CNNs
3.3 Graph Node Selection Algorithm
4 Results
4.1 MLP Inference Path
4.2 Cluster Similarity Across Layers
4.3 CNN Inference Graphs
5 Conclusions
References
An End-to-End OCR Text Re-organization Sequence Learning for Rich-Text Detail Image Comprehension
1 Introduction
2 Related Work
2.1 Sequence Modeling
2.2 Document Analysis
3 Re-organization Model Architecture
3.1 Task Definition

3.2 Graph Construction
3.3 Graph Convolutional Encoder
3.4 Pointer-Based Attention Decoder
3.5 Sinkhorn Global Optimization
4 Experiments
4.1 Dataset
4.2 Baselines
4.3 Evaluation Metrics
4.4 Results and Analysis
4.5 Real User Experience
5 Conclusion
References
Improving Query Efficiency of Black-Box Adversarial Attack
1 Introduction
2 Related Work
3 Proposed Neural Process-Based Black-Box Attack
3.1 Preliminaries of Neural Process
3.2 Pre-training of Neural Process
3.3 Overview of the Proposed NP-Attack
3.4 Optimization of NP-Attack